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José M. Álvarez-Monzoncillo (PhD, Complutense University of Madrid) is
full-time Professor of Audiovisual Communications at Rey Juan Carlos University
in Madrid. He is currently the Course Director of a Master’s degree in Television
Journalism and Director of the Infocent research group. His research and teaching
interests include international media strategies, media branding, media business
models, media and cultural policy, social media and media industries/cultural
industries.
YouTube, Instagram, Facebook, Vimeo, Twitter, and so on, have their own logics,
dynamics and dierent audiences. This book analyses how the users of these social
networks, especially those of YouTube and Instagram, become content prescribers,
opinion leaders and, by extension, people of influence.
What influence capacity do they have? Why are intimate or personal aspects
shared with unknown people? Who are the big beneficiaries? How much is vanity
and how much altruism? What business is behind these social networks? What
dangers do they contain? What volume of business can we estimate they generate?
How are they transforming cultural industries? What legislation is applied? How
does the legislation aect these communications when they are sponsored? Is the
privacy of users violated with the data obtained? Who is the owner of the content?
Are they to blame for “fake news”? In this changing, challenging and intriguing
environment, The Dynamics of Influencer Marketing discusses all of these questions
and more.
Considering this complexity from dierent perspectives: technological, eco-
nomic, sociological, psychological and legal, the book combines the visions of
several experts from the academic world and provides a structured framework
with a wide approach to understand the new era of influencing, including the
dark sides of it. It will be of direct interest to marketing scholars and researchers
while also relevant to many other areas aected by the phenomenon of social
media influence.
The Dynamics of Inf luencer Marketing
This series welcomes proposals for original research projects that are either single
or multi-authored or an edited collection from both established and emerging
scholars working on any aspect of marketing theory and practice and provides an
outlet for studies dealing with elements of marketing theory, thought, pedagogy
and practice.
It aims to reflect the evolving role of marketing and bring together the most
innovative work across all aspects of the marketing ‘mix’ – from product develop-
ment, consumer behaviour, marketing analysis, branding and customer relation-
ships, to sustainability, ethics and the new opportunities and challenges presented
by digital and online marketing.
22. Evaluating Social Media Marketing
Social Proof and Online Buyer Behaviour
Katarzyna Sanak-Kosmowska
23. Charity Marketing
Contemporary Issues, Research and Practice
Edited by Fran Hyde and Sarah-Louise Mitchell
24. The Dynamics of Influencer Marketing
A Multidisciplinary Approach
Edited by José M. Álvarez-Monzoncillo
25. Consumer Ethnocentrism, Country of Origin and Marketing
Food Market in Poland
Paweł Bryła and Tomasz Domański
26. Digital Consumer Behaviour in Europe
Implications of Technology, Media and Culture on Consumer Behavior
Edited by Małgorzata Bartosik-Purgat and Nela Filimon
For more information about this series, please visit: www.routledge.com/
Routledge-Studies-in-Marketing/book-series/RMKT
Routledge Studies in Marketing
The Dynamics of Inf luencer
Marketing
A Multidisciplinary Approach
Edited by José M. Álvarez-Monzoncillo
First published 2023
by Routledge
4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN
and by Routledge
605 Third Avenue, New York, NY 10158
Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2023 selection and editorial matter, José M. Álvarez-Monzoncillo;
individual chapters, the contributors
The right of José M. Álvarez-Monzoncillo to be identified as the author of
the editorial material, and of the authors for their individual chapters, has
been asserted in accordance with sections 77 and 78 of the Copyright,
Designs and Patents Act 1988.
The Open Access version of this book, available at www.taylorfrancis.com,
has been made available under a Creative Commons Attribution-Non
Commercial-No Derivatives 4.0 license.
Trademark notice: Product or corporate names may be trademarks or
registered trademarks, and are used only for identification and explanation
without intent to infringe.
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library
Library of Congress Cataloging-in-Publication Data
A catalog record has been requested for this book
ISBN: 978-0-367-67890-6 (hbk)
ISBN: 978-0-367-68091-6 (pbk)
ISBN: 978-1-003-13417-6 (ebk)
DOI: 10.4324/9781003134176
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List of contributors vii
Introduction 1
JOSÉ M. ÁLVAREZ-MONZONCILLO
1 Making use of digital methods to study influencer marketing 5
PRINCE CHACKO JOHNSON AND CHRISTIAN SANDSTRÖM
2 The marketing of UGC, media industries and business
influence: The Hydra of Lerna and the Sword of Heracles 19
JOSÉ M. ÁLVAREZ-MONZONCILLO AND MARINA SANTÍN
3 The power of algorithms and keys of participation 39
JOSÉ ESTEVES
4 Reviewing the Commercial and Social Impact of Social
Media Influencers 60
CHEN LOU, TIFFANY CHEE AND XUAN ZHOU
5 The evolution of the influence business 80
ANTONIO BARAYBAR FERNÁNDEZ
6 Influencer marketing dynamics: The roles of social
engagement, trust, and influence 99
SYLVIA CHAN-OLMSTED AND HYEHYUN JULIA KIM
7 How Instagram and YouTube users share news: Algorithms,
monetization and visibility on social media 123
JONATHON HUTCHINSON AND TIM DWYER
Contents
vi Contents
8 A cross-pollination of fame? Star athletes and influencers
onInstagram 143
EMILIO FERNÁNDEZ PEÑA, NATIVIDAD RAMAJO AND ADOLFO NIETO
9 Crowd influences in branded communities:
The case of CrossFit 165
ANNE MORAWIETZ, ADELE BERNDT AND TOMAS MÜLLERN
10 Three key practices of image building in entrepreneurial
identity work of freelance journalists 183
SVEN-OVE HORST AND TOON BROUWERS
Index 203
Antonio Baraybar Fernández is Associate Professor of Audiovisual
Communication and Advertising at the Rey Juan Carlos University, Madrid
and has a PhD in Information Sciences from the Universidad Complutense de
Madrid. His research and teaching interests are in the area of the economics of
communication, specifically marketing, communication management in organ-
izations, new, emerging business models fostered by new technologies, and the
eects of persuasive communication from the perspective of neuroscience. For
more than a decade he worked in private television, as head of corporate mar-
keting management at Antena 3 Television.
Adele Berndt is Associate Professor at Jönköping International Business School
(JIBS), Sweden and an aliated researcher at Gordon Institute of Business
Science (GIBS) at the University of Pretoria. Her research focuses on the inter-
section of consumers and branding in diverse product and service contexts,
lecturing and publishing in a range of journals in these areas. She is a member
of the Academy of Marketing Science and serves on the boards of various aca-
demic journals.
Toon Brouwers is Teacher of TV-Journalism, HU University of Applied Science.
He is passionate about audiovisual media, and entrepreneurship education
related to journalism. He has extensive experience about being a journalist and
producing for dierent kinds of media, such as TV, radio, video, and photogra-
phy. He is the owner of a startup called “Toon op TV”, which centers on tv
production. He aims to share his knowledge about how to become a media
entrepreneur with his students. In his free time, he develops podcasts about
innovative topics, and is very hard to beat in gaming.
Dr. Sylvia Chan-Olmsted is the Director of Media Consumer Research in the
College of Journalism and Communications at the University of Florida. A
Professor of Media Management, Dr. Chan-Olmsted’s research expertise
includes emerging media consumption behavior, brand and media engagement,
brand trust, marketing strategy of media firms, and AI applications in media and
marketing communications.
Contributors
viii Contributors
Tiany Chee, Undergraduate Student, Wee Kim Wee School of Communication
and Information, Nanyang Technological University, TIFF0015@e.ntu.edu.sg
Tim Dwyer is the author of several books including Sharing News Online:
Commendary Cultures and Social Media News Ecologies (with Fiona Martin) and
Convergent Media and Privacy. He researches media and communications indus-
tries, algorithmic mediatisation, and pluralism in news media platform
transformations.
José Esteves is full-time Professor of Information Systems at IE Business School,
and Associate Dean of full-time MBA programs. He holds a PhD in software
and information systems from Universidad Politecnica de Catalunya, Barcelona,
Spain. He is also a master in information systems, Universidade do Minho,
Braga, Portugal, and he has a Diploma in Business Administration (DBA) and a
minor in financial management from Instituto Superior de Tecnologia
Empresarial, Porto, Portugal. In addition to his research and teaching, he con-
tinues to act as a consultant to a number of companies.
Emilio Fernández Peña is founding Director of the Sport Research Institute at
the Universitat Autònoma de Barcelona. He is Head of the Olympic Studies
Centre at the university and a research collaborator of the International Olympic
Committee. He is a member of the Development Working Group set up to
design the educational oering of the new International Olympic Academy.
Sven-Ove Horst is Assistant Professor for Media and Creative Industries at
Erasmus University Rotterdam, and Visiting Professor at Universidad de
Navarra, Pamplona. His research centers on strategic media management,
media entrepreneurship, and organization theory, and has been published in,
for example, the International Journal on Media Management, the Journal of Media
Business Studies, and the Journal of Media Management and Entrepreneurship. He
likes exploring emergent phenomena, and is currently taking a deep dive into
cryptocurrencies, social media and investing.
Jonathon Hutchinson is Senior Lecturer in Online Communication and Media
at the University of Sydney and a Chief Investigator on the Australian Research
Council Discovery Project, Media Pluralism and Online News. His research
explores Public Service Media, cultural intermediation, everyday social media,
automated media, and algorithms in media. He is Editor of the Policy & Internet
journal, and the Treasurer for the Australian and New Zealand Communication
Association (ANZCA).
Prince Chacko Johnson is PhD Candidate at Jönköping International Business
School. Johnson is interested in artificial intelligence and its eects on firms. He
makes use of digital media databases, social media data, and a combination of
analytics and web scraping techniques.
Hyehyun Julia Kim is a PhD student in the Department of Advertising at the
University of Florida. She received her M.A. in Mass Communications from
Korea University and B.A. in English Literature from University of British
Contributors ix
Columbia. Her research interests include consumer perceptions of influencer
marketing and consumer brand relationships. Prior to joining the University of
Florida, she worked as a media planner at Mindshare Korea.
Chen Lou (PhD, Michigan State University) is Assistant Professor of Integrated
Marketing Communication, Wee Kim Wee School of Communication and
Information, Nanyang Technological University, chenlou@ntu.edu.sg
Anne Morawietz is a doctoral student in marketing at Jönköping University,
Sweden. Her research focuses on consumer engagement, experiences, and
transformation in branded communities of practice. She has a particular interest
in fitness communities, for example CrossFit.
Tomas Müllern is Professor in Business Administration at Jönköping International
Business School. His research in marketing is focused on sustainable marketing,
with a special focus on marketing communication practices, promoting sustain-
ability messages and how consumers react to those.
Adolfo Nieto is a junior researcher in the Sport Research Institute at the
Universitat Autònoma de Barcelona. He has been a guest researcher at Monash
University (Melbourne, Australia) and he is currently conducting his doctoral
research on sports personal brand management on social media.
Natividad Ramajo is Senior Lecturer in, and Director of, the Department of
Audiovisual Communication and Advertising at the Universitat Autònoma de
Barcelona. Her research activity focuses on social media and sport, media gen-
der studies, and teenagers’ interaction with audiovisual media.
Christian Sandström is Senior Associate Professor at Jönköping International
Business School and the Ratio Institute. His research concerns the interplay
between technological and institutional change along with the related strategic
challenges for firms and policymakers. Sandström has made use of both social
media data and digital archives in his research.
Marina Santín is Associate Professor and Researcher at the Department of
Communication Science and Sociology at the Rey Juan Carlos University. She
has a doctorate in Communication and a degree in Journalism and Law from
the Universidad Complutense of Madrid. She specializes in the study of the
production and distribution of media content. Her main line of research focuses
on the analysis of the journalistic profession and the application of professional
deontology therein.
Xuan Zhou (M.S., University of Edinburgh), PhD Candidate, Wee Kim Wee
School of Communication and Information, 05-13, Nanyang Technological
University, ZHOU0352@e.ntu.edu.sg
DOI: 10.4324/9781003134176-1
Introduction1
José M. Álvarez-Monzoncillo
The time we spend connected to social networks is growing more and more.
Thephenomenon of social media entertainment (SME) is capturing the attention
of the traditional media audience and of the entertainment industry in general.
Inmany countries, the average amount of time dedicated to social networks per
day is in excess of one hour. The study of this new style of consumption is complex
because during that time users also share content from the media and entertainment
industry: news, video-clips, and so on. A large number of television companies
and many newspapers have also opened channels on platforms such as YouTube in
a search for visibility and, consequently, possible revenue.
However, those professional contents live alongside amateur ones. This has been
the key element of change in communication this century: users can create their
own content and distribute it freely on exchange platforms, thus giving them
considerable visibility. The “sharing economy”, the idea that certain products
should be free (the common goods), and the free playing of digital products (zero
marginal cost) have transformed the current ecosystem of communication and
digital leisure.
At the same time, something which is transcendental for understanding this
transformation has changed: the personal information which search engines or
social networks obtain is being sold to others in order to optimize marketing. The
digital footprint has economic value – something which clashes with the right to
privacy and intimacy.
In a scenario dominated by platforms such as YouTube, Instagram, Facebook,
Twitch, Snapchat, Twitter, and so on, many users have appeared and are actively
participating with each of them: vloggers, streamers, YouTubers, influencers,
Instagrammers, gameplayers, TikTokers, and so on. Each platform has its own
features and its own specialization and users take advantage of them in dierent
ways with dierent profiles and dierent aims. Some create content for fun, others
for altruistic motives and others to make money – often involving their hobbies.
In the context of this mixture, the book focuses on the phenomenon of influ-
encers. It is a blurred concept with a lot of stereotypes. Most of the population
would associate it with femininity and fashion & beauty. However, it is obvious
that the business of influence goes beyond that and can aect any industry, as has
been the case historically. In recent years, new ways of influencing have appeared,
2 José M. Álvarez-Monzoncillo
rendering the traditional ones obsolete. There are many examples but the Twitter
accounts of Donald Trump or Elon Musk are paradigms of how, by using a plat-
form, you can reach a greater level of influence than via conventional media, as the
so-called “fourth estate”. Lobby groups are regulated by states. Advertising and
public relations agencies have also attempted to gain influence over consumers’
purchasing decisions. Companies themselves have always developed strategies and
campaigns to influence buyer behaviour. That’s why the term “influencer”, as a
concept and business, is so broad and has had a long history, as can be seen in
Chapter 5 on the evolution of the influence business.
When influencers reach a critical mass of followers, companies appear which are
willing to invest in many channels to promote their products in such a way that the
production of content is becoming professional as production costs rise. This phe-
nomenon would not be possible without platforms which bring one and the other
together. They are intermediaries in the value chain and, consequently, they
receive a percentage of the income – the figure is normally around 20 per cent.
Followers tend to participate actively with comments or likes, creating certain
community links. Platforms have personal information about the digital footprint
so they can market and advertise programmatically. At the same time some com-
panies have an interest in sponsoring content creators to link the brand image to
some influencers. Normally contents are produced professionally to capture more
attention and increase influence. On many occasions that poses ethical problems
and causes a fall in the ability to influence.
The logic of brand communities is also very interesting since marketing has
several peculiarities which are linked to exchanges and the participation of com-
munity members.
The relationships which are established are emotional as what brings people
together is a common idea, taste or hobby. Feelings of belonging are, therefore,
generated which is why people share and collaborate. Personal information from
search engines and internet activity plus the feelings of belonging to a group which
develop around an influential person is all of interest to organizations because they
can target their marketing. These are new ways of functioning. Years ago a large
manufacturer of sportswear would sign up a leading sportsperson so that their
adverts would appear all over the media whereas today they contract them so that
on their blog or Instagram profile they give an opinion, make comments or post
photos to influence their followers. These are new opportunities which firms are
working to optimize and, at the same time, there are new features which are less
well known such as the factors which determine the engagement between follow-
ers and influencers.
On the other hand, intermediaries must give influencers a financial reward and
manage advertising spending in exchange for a percentage. These control the
information in the process in an automated way. The true power lies in the control
of algorithms.
No-one doubts that this opens up an interesting field of academic and applied
research into the marketing of influencers. Many studies have already been carried
out and research results published. This book contains ten chapters which analyse the
marketing of influencers with a multi-disciplinary and complementary approach.
Introduction 3
The first chapter, “Making use of digital methods to study influencer market-
ing” by Prince Chacko Johnson, Christofer Laurell and Christian Sandström, dis-
cusses how digital media and specifically social media open up new opportunities
for scholars, describing and discussing them in further detail. We also address the
possibilities of making use of AI and machine learning in order to analyse large
amounts of data present in social media.
At the same time, media industries are looking for a subscription model which
allows them to work with higher costs to adapt to the new digital environment
with its numerous competitors. In Chapter 2, Professor Santín and I analyse this
new ecosystem made up of platforms, traditional media and UGC. The chapter
also explains the codes used in the marketing of influencers as well as the problems
derived from aspirational work in the middle of a wave of narcissism. We believe
that the power of the hydra of platforms may be changed by the power of the
people, technological innovation itself and the powerful level of entrepreneurship
which exists in the current ecosystem.
José Esteves, in Chapter 3 of the book analyses the use of algorithms in social
media. Social media services are the kings of algorithm usage, namely for content
curation, user data collection and content creation. First, we describe these main
applications of algorithms. Then, we outline challenges of using algorithms in
social media, in particular transparency and objectivity.
In Chapter 4, considering the large social capital of social media influencers, we
believe that there is much untapped potential beyond the commercial sphere.
Therefore, a systematic literature review that aims to uncover the greater impact of
social media influencers – both commercially and socially – is conducted by Chen
Lou, Tiany Chee and Xuan Zhou. The chapter also describes the methodology
and framework that form the basis of the literature review. This is followed by a
synthesis of the findings gathered, before concluding with the future direction for
research and practical implications.
In western culture, the concept of influence has been linked from the very
beginning to the power of persuasion. Influence seeks to confirm or change the
behaviour of others, either to prevent other coercive methods or to justify their
use. The development of mass media communication facilitated the appearance of
a whole new industry of persuasion and the surge in advertising activity and
public relations. Many of these practices have adapted to (or are trying to) the
current digital environment; but new opportunities are also appearing, there are
modifications of the players who make up the market and new business formulas
are being explored. This path can be seen in Chapter 5 by Antonio Baraybar
Fernández.
The basis of Chapter 6, by Sylvia Chan-Olmsted and Julia H. Kim, is the true
potential of influencers. Their aim is to analyse the marketing dynamic of influ-
encers and discuss the roles of four emotion-laden factors: engagement, trust,
authenticity and relationship, as well as how they might collectively influence the
eectiveness of influencer marketing.
In Chapter 7, Jonathon Hutchinson and Tim Dwyer explore the complexities
of news production and distribution across the highly popular social media plat-
forms, YouTube and Instagram. They argue that amidst unprecedented calls to
4 José M. Álvarez-Monzoncillo
make platform providers accountable for the content that is created, distributed
and consumed within our societies, there is an urgent need to better understand
their business models in terms of their platform aordances. In this chapter,
Hutchinson and Dwyer outline how the constraints surrounding digital interme-
diation, specifically those that determine and manage the content monetization
process, are shaping how content creators produce and publish their work on these
platforms. They provide details of this dynamic digital intermediation process,
contextualizing it through the lens of South Korean Mukbang content producers
before then examining three international YouTube news providers.
In Chapter 8 Emilio Fernández Peña, Natividad Ramajo and Adolfo Nieto
analyse the interactions on Instagram of sports stars and famous people. Their
work deals with Instagram in the context of the system of social and digital media
which contributes to the creation of a personal brand and they explore what they
refer to as “cross-pollination”. In other words, they study in depth the comple-
mentary action and feedback between the dierent participations of the personal-
ity on social networks and their activity on Instagram.
Chapter 9 by Anne Morawietz, Adele Berndt and Tomas Müllern builds on the
fact that there has been limited published research into the community as well as
the form and types of influence that exist within and between the various mem-
bers of the CrossFit community. Their work aims to provide a conceptual model
that seeks to develop and illustrate the nature of these dynamic influences based on
the CrossFit context. In this chapter we use the CrossFit example to describe the
phenomena of crowd influencing (moving within and between online and oine
communities and its members and the range of brands), and how it has contributed
to make CrossFit a global success story.
In the final chapter, Sven-Ove Horst and Toon Brouwers dive into the process
and practices of early career journalists who are building their identity as freelanc-
ers and early-stage entrepreneurs in the media field. Based on recent interview
data with Dutch journalism students, they explored what kind of practices related
to social media are important and found that storytelling about themselves, respon-
sive networking and managing social media mindfully are key for image building.
All that remains is to express our gratitude to all those who have taken part in
the book, and for all the excellent work they have carried out.
Note
1 This publication sets forth the results of research carried out as part of the R&D project
CSO2012-37976 of the Spanish Ministry of Economy and Competitiveness.
DOI: 10.4324/9781003134176-2
1 Making use of digital methods to
study inf luencer marketing
Prince Chacko Johnson and Christian Sandström
1.1 Introduction
Digitalization and the emergence of social media has fundamentally altered the
media landscape. The growth of these communication channels has spawned the
rise of influencers, influencer marketing and the related phenomena of social
media celebrities, a.k.a. Instafamous (Khamis et al., 2016). Relatedly, big data and
various approaches that make use of analytics are still in the process of transforming
media in terms of customer experiences and the underlying competitive
dynamics.
Navigating and understanding the new era of influencer marketing can be a
daunting task. The rise of digital and computational methods that facilitate large-
scale data collection and analysis to be performed automatically (Flaounas, Ilias,
Omar et al., 2013) open up novel opportunities to study influencer marketing.
These methods could be classified as digital methods, as they tend to capture
developments, while approaching the web as a data set (Rogers, 2015).
At present, little is known regarding how these novel sources of data and related
methodological approaches can be applied in order to study influencers and influ-
encer marketing. While a couple of contributions have been made discussing how
digital advances can inform management research more generally (e.g. Eriksson
etal., 2019), there is presently a need for more research addressing how this can be
done. In a broader sense, digitalization is creating new opportunities to conduct
research with potentially significant eects across the social sciences (Kosinski
etal., 2016), but little is presently known regarding how these advances can be
applied in order to study influencer marketing.
In this chapter, we describe how digital data and digital methods can be used in
order to study influencer marketing, media management and business administra-
tion. We begin by providing a background to influencer marketing. Next, we
describe a collection of challenges that have historically been hard to deal with in
academic research. Then, we describe two methodological approaches and discuss
how they can be applied to study influencer marketing. We subsequently discuss
some of the potential benefits of these approaches. Last, a concluding remark is
provided.
6 Prince Chacko Johnson and Christian Sandström
1.2 Background: influencer marketing
We begin this background by describing the current state of research in influencer
marketing. Social media celebrities are usually perceived as distinct from real-world
celebrities and can be referred to as micro celebrities (Abidin, 2016; Brown and
Hayes, 2008). Influencer celebrities can be defined as “people who built a large
network of followers and are regarded as trusted tastemakers in one or several
niches” (De Veirman et al., 2016, p. 1). As these celebrities are interpreted as more
real and accessible than conventional celebrities, consumers tend to identify more
with them and imitate them to a greater extent (Tran and Strutton, 2014).
Research on influencers and influencer marketing has been highlighted recently
by scholars as an important research area to study further (Taylor, 2020). Market
research also points at influencer marketing as an important and growing phenom-
enon. A study written by the Association of National Advertisers (ANA) (2018),
observed that 75% of consumers were reached by influencer marketing, and 36%
of them regarded it as eective. Data on youth consumer behavior also suggest that
influencers are becoming increasingly important as a marketing channel. YPulse
(2020) reports that the likelihood of 13–18-year-olds to follow influencers online
has increased by 54 to 70% in only one year. Below, we turn to a couple of topics
that need further investigation.
1.2.1 Influencers as opinion leaders
Literature on diusion and adoption of innovations has highlighted the critical role
opinion leaders play in the introduction of new products, behaviors and services
(Rogers, 2010). An opinion leader can be defined as an actor that others within a
social system listen to and follow. Sport stars, celebrities and models often take on
the role of opinion leaders as crowds tend to pay attention to them. A lot of mar-
keting activities such as advertising is built on the critical role of influencers in
order to convince adopters.
Influencers can almost by definition be conceptualized as a form of opinion
leaders. With a large base of followers, they have obtained a high status, are a form
of semi-celebrities that can introduce novel products or trends and reach a wider
audience. Plenty of research has been done by now concerning opinion leaders
online, but most of it has so far concerned bloggers and covered a range of dier-
ent methods for detecting opinion leadership (Deng et al., 2013; Li et al., 2013;
Feng, 2014). Relatedly, we also see some research on opinion leadership on social
media platforms such as Twitter (Dubois and Ganey, 2014) particularly concern-
ing political issues (Park and Kaye, 2017). As of now, however, these issues remain
largely unexplored within the area of influencer marketing.
1.2.2 The practices of influencers
A key question concerning influencer marketing concerns what influencers actu-
ally do. In, for example, strategic management, practice-oriented perspectives have
emerged over the past decades (Whittington, 1996; Whittington, 2006), arguing
Making use of digital methods to study influencer marketing 7
that strategy should be understood as an activity rather than a hard science. Gosling
and Mintzberg (2004) state that:
management is neither a science nor a profession, neither a function nor a
combination of functions. Management is a practice – it has to be appreciated
through experience, in context.
(p. 19)
Practice-oriented perspectives in management may be further enabled to grow and
gain acceptance due to the emergence of both digital research methods and digital
sources of data (Eriksson et al., 2019) as this approach has previously been con-
strained by access to data and the relatively labor-intense nature of this form of
research.
Likewise, we would argue that development of digital data sources and methods
make it possible to study influencers and influencer marketing in new ways, poten-
tially opening up more practice-oriented perspectives as such data sources can
show what influencers actually do. While previous research has shown that influ-
encers who are likeable and popular are more eective brand promoters (De
Veirman et al., 2016) and that influencers with higher levels of trust are more likely
to successfully promote a product (Chen and Yuan, 2019), more knowledge is
presently required in order to show and illustrate how this can be done in practice.
Specifically, we need a better understanding of what practices that actually create
this trust and credibility. As pointed out by Taylor (2020):
academic research on measurement accuracy, eectiveness of various meas-
ures, impact of influencers taking on multiple sponsors, and impact on disclo-
sures can clearly be helpful and the time is ripe for a focus on these issues.
Addressing these issues will be an important driver of the growth of influencer
marketing post-pandemic.
(p. 890)
1.2.3 Internal and external validity
Broadly speaking, the social sciences have faced a tradeo between internal and
external validity. High degrees of internal validity can be seen in some subjects
such as economic history and business history. Parts of management research and
media management scholarship has employed the case study method, which tends
to be suitable for theory development and for the exploration of new topics rather
than for testing hypotheses (Eisenhardt, 1989; Yin, 1994).
Conversely, quantitative approaches prevailing in for instance economics and
areas of business administration such as strategic management may be suitable for
testing hypotheses. Aggregated data may, however, be less useful when the scholar
wishes to disentangle various complex causal relations.
We would argue that the social sciences more broadly have been trapped in a
tradeo between internal and external validity. Attempts at generalization have
often been made at the expense of internal validity, and vice versa.
8 Prince Chacko Johnson and Christian Sandström
1.2.4 Dierences between across media platforms
We currently see and experience the rapid rise and growth of social media and the
media landscape is, as a consequence, being transformed. At present social media
and traditional media are partially overlapping, partially complements and partially
substitutes.
There are presently few studies systematically comparing social and traditional
media. One early contribution pointed out that social media may be more simplis-
tic and less critical towards new phenomena (Laurell and Sandström, 2018), but
generally, more knowledge is needed concerning dierences between these two
forms of media. Here, the role of influencers and influencer marketing needs to be
better understood, especially concerning how they are portrayed in social media
and in traditional media dierently.
Little is also presently known concerning dierences across social media plat-
forms. How do social media platforms dier in terms of the practices employed by
influencers? Below, we describe two digital methodological approaches that can be
applied in order to study influencer marketing. We begin with Social Media
Analytics and then turn to digital media databases.
1.3 Digital data and digital methods
Usage of digital data and digital methods has increased over the past decades. In the
early days of internet penetration, netnographic approaches were popular (Kozinets,
1997, 1998, 2010). The systematic collection and coding of digital data was
employed in fashion blogging and the study of their practices in a number of
studies (Laurell, 2014; Pihl and Sandström, 2013).
As netnography turned out to be rather labor intense, it has been increasingly
displaced by Social Media Analytics (SMA), which can be regarded as an interdis-
ciplinary combination of data collection and analysis that systematically makes use
of social media (Stieglitz et al., 2014). As data can be gathered without intrusion
and in real-time, SMA has been described it as “a kind of living lab, which enables
academics to collect large amounts of data generated in a real-world environment” by some
of the pioneering academics in this field (Stieglitz et al., 2014, p. 90).
An SMA study begins with the choosing of a subject to study. The researcher
decides upon what or who to study, that is, a certain innovation, an influencer, a firm,
or a keyword. In recent times, increasing eorts have neem taken to collect data by
using publicly accessible information from online sources either by using application
programming interfaces (APIs) or scrapping techniques and at the same time the
trend of compiling original datasets of archival data, as well as other media sources, has
gained popularity (Garz, 2020). Here, it is important to choose keywords that gener-
ate sucient amounts of activity. Next, a researcher can either decide to make use of
databases, software services or develop scrapping techniques for data collection.
1.3.1 Tracking and data collection
An SMA study begins with the choosing of a subject to study. The researcher
decides upon what or who to study, that is, a certain innovation, an influencer, a
firm or a keyword. Here, it is important to choose keywords that generate
Making use of digital methods to study influencer marketing 9
sucient amounts of activity. Next, a researcher can either decide to make use of
software services or develop scrapping techniques for data collection.
1.3.1.1 Database approach
The use of academic databases to conduct research on specific topics has become
the new normal of scholarly investigations, where journal articles and other pub-
lications are catalogued which further eliminates the need to manually go through
them (Driedger and Weimer, 2015). While there are multiple curated databases
that compile data from various sources ProQuest and Factiva are two of the major
providers. Both ProQuest and Factiva require subscriptions which are generally
made available by most universities and libraries (Garz, 2020). Articles and publi-
cations from most leading media houses around the globe can be obtained from
these two databases.
1.3.1.2 Data collection software
Next, data collection commences. Here, the researcher can make use of either
APIs or RSS/HTML as ways to access and collect the data. There are several
software services available for doing this, for example, Notified that has been used
for many SMA studies in management (e.g. Laurell and Sandström, 2017, 2018;
Geissinger et al., 2019). Making use of software services has several benefits.
Specific filters can be applied in order to look for users in specific languages, or
with certain user origins such as countries. Also, the fact that social media and the
internet is still comparatively fragmented means that standardized ways to gather
data hold considerable potential.
1.3.1.3 Web scrapping techniques
An alternative to making use of software would be to develop and employ scrap-
ping techniques. Scrapping is a technique that is used for automated data collec-
tion of online data ranging from media from news outlets, social media, app
reviews, websites, and so on (Marres and Weltevrede, 2013). It is a great approach
to extract unstructured and cluttered data from the internet and further transform
that data into structured, organized, and uncluttered data that can be stored and
further analysed in a database (Sirisuriya, 2015).
Mitchell (2018) defines web scrapping as a practice of gathering data through
any means other than a program interacting with an API; this could be done by
writing an automated program that queries a web server, requests data, and then
passes the data to extract needed information. The idea behind scrapping is to
collect data from unstructured websites and outlets and bring it together in struc-
tured formats such as spreadsheets, comma-separated values (CSV) files, etc.
(Figure 1.1).
There are various tools and techniques that can be used for web scrapping. Web
data is generally scrapped using hypertext transfer protocol (HTTP) programming,
hypertext markup language (HTML) parsing, through a web browser along with
an automatic bot or web crawler (Zhao, 2017). Previous studies such as (Johnson
10 Prince Chacko Johnson and Christian Sandström
et al., 2022) has used python to scrap large amounts of data from online newspaper
outlets. Owing to the large amount of data that is continuously generated on the
world wide web, web scraping is accepted and acknowledged widely as an ecient
and powerful tool in collecting data (Mooney et al., 2015; Bar-Ilan, 2001; Zhao,
2017). One of the tools called scrapy is an open-source application that has gained
traction for web scrapping over the last few years which can be written in python
(Thomas and Mathur, 2019).
While web scrapping is a powerful tool which ideally helps us to collect large
amounts of data, there have been ethical debates around it such as copy right
concerns (O’Reilly, 2006) and terms of service (Fisher et al., 2010; Zhao, 2017).
1.3.2 Data analysis
Having finished data collection, dierent forms of content analysis techniques can
subsequently be employed (Silverman, 2006). Here, both structured data such as
account details and unstructured data such as text can be used. First, data sets are
studied by looking at how the data is spread across dierent social media platforms.
Next, dierent techniques for data analysis can be employed, including manual
coding, statistical and computational methods and more sociological approaches
such as Social Network Analysis (SNA).
Below, we expand further on dierent techniques for data collection and
analysis.
1.3.2.1 Manual coding techniques
Coding of material can be done manually. In the first step, the researcher needs to
remove material that concerns other issues. Next, a coding scheme can be
employed. What scheme to use ultimately depends on the research objective at
hand. In previous literature, there are examples of applications of institutional the-
ory to track and measure practices (e.g., Laurell and Sandström, 2017). Other
examples include studies of entrepreneurs and how they position themselves on
Twitter (e.g., Obschonka, 2017). As manual coding schemes can be quite labori-
ous, we next describe some software-based techniques that can be employed.
1.3.2.2 Software-based analysis
There are various reasons why one would want to opt for an automated system for
content analysis of text, as it is known that human decision makers are potentially
subject to influence that can’t be reported (Nisbett and Wilson, 1977). The usage
Figure 1.1 Basic flowchart of web scrapping derived from Mitchell 2018.
Websites, blogs,
online newspaper
outlets, etc. (internet)
Web scrapper
techniques and
tools
Structured data
Making use of digital methods to study influencer marketing 11
of software to assist and support researcher in the analysis of data has been very
important over the years, such as statistical package for the social sciences and
NVivo has been the most commonly used software for analysis (Sotiriadou et al.,
2014). There is an extensive requirement of time and eort in the process of
human content analysis where the code books or dictionaries must be validated,
coders must be rained, intercoder reliabilities must be tested, and so on (e.g.,
Weber, 1990), whereas automating the analysis process would reduce the time and
eort but also simultaneously allow more rapid and frequent analysis and reanalysis
of text (Smith and Humphreys, 2006). Content analysis has been applied in dier-
ent ways and techniques such as Hyperspace Analog to Language (HAL) (Lund,
1997), Latent Semantic Analysis (LSA) (Landauer et al., 1998), Leximancer (Smith,
2000) are common algorithms used to take text and extract concepts for analysis.
Over the years AI techniques such as data mining (Liu, 2007), natural language
processing (NLP) (Manning and Schutze, 1999; Yi et al., 2003), computer visual
analytics (Butler, 2008), and machine learning (ML) (Shawe-Taylor and Cristianini,
2004) can be used for analysis. Text mining tools and concept maps are techniques
and tools that are well established methods for extracting key concepts from a tex-
tual corpus and further displaying them in a graphical representation (Stockwell et
al., 2009). Automated content analysis (ACA) is referred to as a suite of algorithms
that uses models such as topic models or concept mapping models (Blei, 2012) to
explain and understand hidden composition of a body of text or literature (Nunez-
Mir et al., 2016). Many tools have been developed to facilitate the use of topic
modelling and concept mapping for ACA over time which included R packages,
python libraries and various software solutions. Some tools such as Mallet, Standord
TMT, topic models, and so on, require coding skills while on the other hand
Leximancer, Gavagai and Google TMT feature user-friendly graphic user inter-
faces (Nunez-Mir et al., 2016). These automated analysis techniques can be either
utilized by individual researchers or by service providers that use these techniques
and help with scientific research. Leximancer and Gavagai are two of the many
services that have been leading automated analysis and which are described below.
1.3.2.2.1 LEXIMANCER
Leximancer is a relatively new software that can perform ACA which transforms
lexical co-occurrence information from free text and natural language into seman-
tic patterns in an unsupervised manner with no pre-conceptions while the analysis
emerges from the data (Cheng and Edwards, 2019). It is semi-automated content
analysis tool that can be used to analyse either a single document or even a collec-
tion of documents by identifying the key terms by using word frequency and
co-occurrence usage (Smith, 2000). It goes beyond keyword searching but discov-
ers and extracts thesaurus-based concepts from the text data; an external dictionary
can be used however it is not mandatory. Based on the data concept, maps and
themes are generated in steps as seen in Figure 1.2. It analyses text data while using
statistics-based algorithms to automatically analyse text and then derives visual
representations (Smith and Humphreys, 2006). The concepts are then streamlined
into dierent themes and then representational figures are generated.
12 Prince Chacko Johnson and Christian Sandström
1.3.2.2.2 GAVAGAI
Gavagai Explorer analyses texts in 47 dierent languages which is commercially
available for users and they provide end-to-end analysis of unstructured data while
dealing with various components such as topic clustering, sentiment analysis, and
concept modelling (Espinoza et al., 2018). Topic clustering is done based on lexi-
cal cues and is used to detect prevalent themes and topics within the dataset, fol-
lowed by concept modelling and sentiment analysis (Afsarmanesh et al., 2019).
After the topic clustering, Gavagai also oers an extension where seed words can
be entered and the user is presented with similar terms acquired from an online
distortional semantic model which is constantly updated within data from various
data sources such as trendiction, twingly, gnip, notified, and so on (Sahlgren et al.,
2016). While Gavagai Explorer helps with analysis, it also oers data scrapping
opportunities from various public sources.
The whole motive of these automated analysis techniques is to empower
research, analysts, and data scientists rather than replacing them. These approaches
and techniques help to increase coverage and consistency throughout the analysis
process, making it more reliable. They could also be used as an approach to analyse
free text responses within surveys which would otherwise be a tedious task
(Espinoza et al., 2018).
1.4 Discussion
Having outlined above how social media data, media data, SMA and some related
software services can be applied for the study of influencer marketing, we now
describe some of the benefits of doing so.
1.4.1 Uncovering influencers as opinion leaders
How do influencers become opinion leaders? What characterizes those that suc-
cessfully become opinion leaders as opposed to those who don’t? How does word
of mouth spread across social media? We see ample opportunities to integrate
research on diusion of innovations and opinion leadership with influencer mar-
keting by making use of digital methods and digital data.
Figure 1.2 Adapted from Crofts and Bisman 2010.
Word
Theme
Theme
Concept
Concept
Concept
Word
Word
Word
Making use of digital methods to study influencer marketing 13
As data is digital and available online on various accounts, the emergence and
growth of successful influencers can be tracked and studied in greater detail. Their
followership, their activities and marketing eorts can both be studied historically
and in real-time. Within the applied sciences, methods have been developed to
make use of expert systems in order to detect and identify opinion leaders
(Bamakan et al., 2019). Others have studied the role of blogs in shaping word of
mouth marketing activities. Li and Du (2011) state that previous research high-
lighted the importance of social networks, but struggled to identify opinion lead-
ers. They develop a framework to find opinion leaders based on their blog contents,
relationships and activities. We see ample opportunities to develop a similar
approach for studying influencers as opinion leaders and welcome such eorts in
the coming years.
1.4.2 Exploring practices of influencers
By making use of SMA and related software services, we would argue that it is
possible to study the practices of influencers. Previous scholarly work has done so
with fashion bloggers and the blogosphere, being able to disentangle the share of
private messages, commercial messages and how bloggers integrate brands into
their daily lives (Pihl and Sandström, 2013). Similar studies can be employed on
influencers in the social media landscape, potentially with less eort regarding data
collection and analysis as this can be done by making use of software. We would
also suggest that computational approaches can decipher what forms of entries
result in more or less likes, shares and so on.
1.4.3 Comparisons across media platforms
As stated previously, little is presently known regarding how influencer practices
dier across platforms. Access to, and systematic data collection across media plat-
forms enable such studies to be pursued. Previous research has measured and doc-
umented how social media diers from traditional media (Laurell and Sandström,
2018). Portrayal of influencers and dierences between these two forms of media
can be studied in a similar fashion. We also see that it has been possible to compare
dierent social media platforms such as Twitter, Facebook, Instagram, Forums and
Blogs with regard to their contents (Laurell and Sandström, 2021). As SMA ena-
bles the systematic collection of data across platforms, we see that this approach
holds considerable potential for the study of influencer marketing and how it dif-
fers across platforms.
1.4.4 Increasing internal and external validity
Social sciences have broadly faced a tradeo between internal and external validity
(Eisenhardt, 1989). The emergence and growth of digital data and digital methods
makes it possible to partially transcend this tradeo. Detailed ethnographic
approaches can be combined with aggregated studies of patterns across media
platforms, across dierent influencers and dierent sectors of the economy.
14 Prince Chacko Johnson and Christian Sandström
The application of software techniques and programmes such as Gavagai and
Leximancer described above is likely to enable increased internal and external
validity.
1.4.5 A need for new skill sets among researchers
Having identified several directions of future research that are enabled by digital
data and digital methods, we also see a need for further skill development among
scholars in order to realize this potential.
An increased need for digital skills is likely to manifest in several ways. First, we
expect that scholars to a greater extent need to master programming languages
such as Python or Java. Beyond this, a command of several digital tools and soft-
ware services will be needed as well as dierent techniques for both manual and
automatic coding techniques.
A shift to more digital scholarship is therefore likely to induce a process of skill
renewal within academia. Doctoral education needs to a greater extent to integrate
digital data usage and methods in order to foster competitive researchers for the future.
This ongoing and coming transition may also potentially alter the role of
researchers. When a scholar relies upon software to conduct an analysis, produc-
tivity might increase substantially. At the same time, a scholar eectively relies
upon the work of programmers and developers, who are unknown and whose
biases or preferences cannot be known, but may nevertheless influence the out-
come of such work. Eects on internal and external validity may be positive in the
long term, yet need to be analysed. We welcome further discussions of how the
role of researchers may be aected by digitalization in the coming years.
1.5 Conclusion
In this chapter, we have described how some digital data sources and methods can
be used in order to study influencers and influencer marketing. Our contribution
is twofold: we provide some tangible guidelines for how to apply digital methods
and, relatedly, we highlight some of the potential benefits of doing so.
As influencers are digital in their operations, it makes sense to study them by
making use of digital data and digital methods. Digital data can be collected in an
unobtrusive manner. The use of various software services for data collection can
make it possible to gather large datasets in real-time with little eort. In this chap-
ter, we also mention and describe some of these services and how they can be
applied to the subsequent analysis.
We identify several benefits of making use of digital data when studying influ-
encer marketing. First, we note that by doing so, it is possible to study the practices
of influencers, highlighting what they actually do. In doing so, we would expect
that research on opinion leaders and diusion of innovations can be further devel-
oped. Second, we note that larger datasets can be collected and analysed, thereby
transcending some of the tradeos between internal and external validity. Third, we
would also suggest that dierences across social media platforms as well as traditional
media platforms can be studied in greater detail by making use of these methods.
Making use of digital methods to study influencer marketing 15
These digital research methods can be very eective and utilized while research-
ing within the space of influencer marketing and social media. Automated concept
mapping, analysis software, text mining tools, and so on are proven eective for
research, to identify trends within the collected data (Stockwell et al., 2009; Garz,
2020). However, as mentioned above, none of these techniques are meant to
replace humans but rather empower them while replacing the frustrating tasks
alone. While working with digital methods for research and analysis, detailed
grammatical information cannot be obtained, but there is an abundance of infor-
mation that is both rich and complex that can be obtained from commercial ser-
vices such as Gavagai or Leximacer (Smith and Humphreys, 2006).
As the adoption of digital tools and methods continues to grow, we argue that
the skill sets of scholars need to change and that competence renewal will become
a source of competitive advantage for researchers in the coming years.
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2 The marketing of UGC, media
industries and business influence
The Hydra of Lerna and the sword of Heracles
José M. Álvarez-Monzoncillo and Marina Santín
2.1 Introduction
The transformation, as brought about by the development of the Internet and
digitalization of production and post-production in the media industry, has trig-
gered the rupture of the value chain, forcing the industry to reinvent itself. This
transformation has empowered users and even allowed them, on occasion, to
become content creators who dare question the traditional power of large media
players. “In recent decades there has been a marked shift from consumer electron-
ics to information technology as the most powerful sectoral force shaping how
media content gets produced, distributed, and experienced” (Deuze & Prenger,
2019: 14). These new means of production, distribution and consumption have
aected the whole of the media industry at dierent levels.
Some media have had to reinvent their business model drastically, redefining
their engagement with their audience, favoring multi-platform distribution. This
has been a dicult process which not only seeks to capture audiences on a one-o
basis but, especially, to generate new opportunities by interacting and creating a
community. This is an uncertain process of adaptation to the digital media envi-
ronment in which competition among all players is tough.
The new ways of communicating which have emerged on the net have made it
possible not only to produce and distribute, but also to interact emotionally with
followers. This empowerment has, perhaps, been one of the most important inno-
vations in the field of communication. User activity not only adds trac – which
is beneficial to telecommunication companies – but the fact that it also allows
them to empathize with a large public is of great interest to brands. This ability to
work with a community of fans of cultural products, with a mix of genres and
reinterpreting a new “acquisition” of others’ work, has been studied thoroughly
(Jenkins, 2006; Jenkins, Ford & Green, 2018). The value is not solely in the pro-
duction and distribution of the product but in the consumption, itself, given that
valuable information is obtained from the interaction.
The apparent ease for creating content has encouraged many to opt for content
creation and the use of dierent platforms when it comes to distribution. That is a
reference to user-generated content (UGC), with its dierent labels: vloggers,
streamers, YouTubers, influencers, Instagramers, gameplayers, Tiktokers and so on.
Each one has their own motivation: altruism, fun, the search for social recognition
20 José M. Álvarez-Monzoncillo and Marina Santín
or the possibility of making money. All of them have taken advantage of platforms
such as YouTube, Instagram, Facebook, Twitch, Snapchat, Twitter … to share
content and, in the case of some of them, they have achieved a commercial value
for their creations, monetizing the relationship which they have established with
their followers or subscribers. In addition, this phenomenon of cultural creation has
often been sprinkled with a considerable dosage of entrepreneurship, innovation,
creativity and sound management which is, in turn, encouraging the so-called
“traditional” media to renew themselves.
The millennials and generation Z have embraced this phenomenon because
they too want to play an active role in the process: new spectators interacting with
other spectators and even with the new idols who have emerged on the net. The
idea of Hollywood vs Silicon Valley is becoming more and more apparent
(Cunningham & Craig, 2019a). As consumption is of great importance and, con-
sequently, attracting advertising, its social importance is also a determining factor
as more and more users take part on social networks.
The close relationship which these creators establish with their followers makes
them of particular importance for brands. They can take advantage of content to
improve their interaction in the area of marketing. The trend is unstoppable and,
day after day, influencers are having more sway in the purchasing decisions and
opinions of many followers. Having said that, this potential brings with it certain
problems as there are fake followers and influencers who lose their eciency when
they promote too many brands. In addition, we must bear in mind the lack of any
legal control over advertising in these spaces. It is a new phenomenon which requires
a much more in-depth study due to its constantly changing nature (Taylor, 2020).
In such a context, the growing power – and not only in economic terms –
which the platforms that make up new digital capitalism have obtained, is a con-
cern. The contemporary platforms “are reconfiguring the production, distribution,
and monetization of cultural content in staggeringly complex ways” (Duy, Poell
& Nieborg, 2019: 1).
Consequently, we are faced with a paradox: on the one hand, the important
phenomenon of the empowerment of audiences that can create and distribute
content and assess products, and, on the other, the control in the hands of very few
platforms which are the ones who reap the benefit. Audiences may co-create
value, but it is not easy for media companies to monetize that value, which is
clearly shifting towards global platforms.
Paradoxically, technological development is destroying the old model of enter-
tainment and media industries. Internet, which, initially, generated too many uto-
pias, has placed the media companies under the control of a small number of global
companies who can define the future rules governing how power and citizens
relate. That means we are living at a very exciting time as reasons for concern and
reasons for hope still co-exist (Hesmondhalgh, 2019).
The dream of long queues was shattered (Napoli, 2016), the dominion of the
blockbuster (Elberse, 2013) and the omnipresent power of the hydra FAANG
(Facebook, Amazon, Apple, Netflix and Google) were confirmed. Just as with the
Hydra of Lerna from Greek mythology – an enormous aquatic snake with venom-
ous breath and many terrifying heads, which lived in the depths – the hydra
The marketing of UGC, media industries and business influence 21
FAANG conditions the underworld of the planet’s information, culture and enter-
tainment, using algorithms, big data, artificial intelligence and virtual reality (VR).
But, as with Heracles, who used a mask to combat the venomous stench, managed
to strike in just the right place with his sword so that no more heads grew back,
technology itself can break the FAANG curse because it can generate new oppor-
tunities and uncertainties. For that reason, we can say that we are living in exciting
times with many empowered Heracles who are experiencing a new golden age of
creativity. The hydra no longer holds all the aces as users can co-create and make
important decisions, taking advantage of the monster’s contradictions.
Entrepreneurship is also considerable, with new initiatives appearing which
question the status quo. It would also be fair to acknowledge that the monster has
some amicable heads which satisfy important user needs and allow progress in that
direction.
This chapter focuses on the change which is taking place in media industries
and the empowerment of people on dierent content-sharing platforms in the
context of digital marketing and the change in consumption habits.
2.2 Media industries: a future with co-creation?
The feeling that the media industry has collapsed is around at present, and not only
because its business models are wobbling – something which is clear and particu-
larly troublesome for the press. The regulation of the media has also become more
flexible, facilitating concentration, and technology firms have overcome the power
of large media conglomerates, taking on a key role when it comes to what news
story we read or which film/series we watch.
The appearance of new ways of communicating, such as Twitter, have triggered
a concern in media industries, making media, “particularly with regard to the role
of news and information that is essential to ideals of public access, shared narra-
tives, and the democratic principles of quality, diversity, and plurality in content
and services oered” (Faustino & Noam, 2019: 177). It should be added that radio
and public television channels have lost their legitimacy and there has been a cut
in subsidies to help cultural industries, these being reduced to a fake support for
entrepreneurship, and innovation using the label of “creative industries”. The
problem has worsened because, in addition, the power of states has been limited in
the face of the new digital capitalism which functions as an oligopoly. There is talk
of a platform-state. The change in media industries has been so stark that “many
of the eects of a post-television system of content creation and distribution
remain unknown” (Strangelove, 2015: 228). An environment of decadence is envi-
sioned and an uncertain future with the growth of globalization. However, this
collapse, at the same time, opens new opportunities in the new environment “that
is characterized by the fragmentation of media options, competition for attention,
and empowered audiences” (Chan-Olmsted & Wang, 2019: 133).
Many studies (Álvarez-Monzoncillo & López-Villanueva, 2014; Holt & Perren,
2009; Mayer, Banks & Caldwell, 2009; Maxwell & Miller, 2012; Albarran, 2013;
Doyle, 2013, Flew, 2013; Starks, 2013; Strangelove, 2015; Picard & Wildman,
2015; Athique, 2016; Chalaby, 2015; Smith & Telang, 2016; Lotz, 2017;
22 José M. Álvarez-Monzoncillo and Marina Santín
McRobbie, 2016; Evens & Donders, 2018; Deuze & Prenger, 2019; Hesmondhalgh,
2019; Kawashima, 2020; Kim, 2021) have tackled this collapse, revealing its con-
sequences at all levels.
Not only are the business models being questioned, but the digital disruption
has also upset the analogical status quo. Technological innovation has modified the
media-making sector, facilitating new support and new ways to produce content,
as well as the change in consumption habits. In this disruptive context, new eco-
nomic agents of significant importance in the media market have appeared:
Internet providers, Internet companies, OTT platforms and social networks.
Media industries have been forced to adapt their business models to this new con-
text in which these new operators, which make inter-connectivity possible, have
joined the market. This new development, as Negroponte had already foreseen,
has led to the integration of computer science, telecommunications and audiovis-
ual industries, a phenomenon which has been called “convergence”.
This process meant the expansion of the information, communication and
technology (ICT) sectors with the borders becoming blurred. In addition, it has
made it possible for media content to be produced, distributed and experienced
very dierently. Convergence has brought with it the debate about net neutrality
to the degree that Internet service providers (ISP) have joined up with the Internet
giants to discriminate content and services by price, bringing into question
Internet access as a public service in line with democratic principles (Pickard &
Berman, 2019).
The new communication market has been made up of new intermediaries who,
in principle, barely add value to content, but seem to be the ones who are taking
most advantage. As Franklin Foer (2017) points out, they have managed to domi-
nate the media, but without actually contracting editors or doing much of any-
thing. They are the champions of the so-called digital capitalism: Samsung, Apple,
Microsoft, Facebook, Google, YouTube, Netflix, Amazon, and so on. The group
of new operators has broken the strategy of interaction – as was the traditional case
– between audiences and advertisers, with a proliferation of side eects and high
complexity (Gabszewicz, Resende & Sonnac, 2015).
Consequently, the digital disruption and these corporations
have nestled themselves firmly in-between media users and producers, mak-
ing each of them co-dependent on their products and platforms for format-
ting, distributing, accessing, and sharing media content … adapt to this new
reality, the values, expectations, and structures of the digital economy come to
co-determine creative decisions and processes.
(Deuze & Prenger, 2019:14)
This control is clearly market-orientated and reduces the pluralism and cultural
diversity which, in the past, were guaranteed by states: the same states which today,
and as a result of globalization, have less ecient maneuverability from that point
of view.
This situation has led to a transcendental change in media industries. The essen-
tial features which had traditionally characterized the sector, such as the mixture of
The marketing of UGC, media industries and business influence 23
state and private ownership, and the rise of corporations; the importance of copy-
right; poor compensation and inequalities for media workers; media production by
the few, distributed to the many and overproduction and blockbusters
(Hesmondhalgh, 2019), have begun to crumble. There was a model which tended
towards concentration and commercialization, but which also boasted some
advantages since there was a certain democratic control (public operators, fees,
etc.) which has been relaxed.
The aforementioned transformation means we are at a crucial stage of great
change. The media face great uncertainty since the classic models are crumbling
and audiences, which are often empowered, show less and less support for histor-
ical media to the point of daring to challenge them. Amateur, professional and
corporate USC of all types are posting content on social networks. It is very pos-
sible that this empowerment of users will lead to frustration with the inherent
diculties in any creative process, but there are opportunities for democratizing
the so-called Social Media Entertainment (SME) and, to an extent, communica-
tion. Everything will depend upon users’ ability to innovate, the boosting of
co-creation by media industries, and the possibility of using legislation to limit the
power of platforms.
In the face of the boom of individuals creating and sharing content on dierent
platforms, some authors have coined the phrase “SME” for “the emerging indus-
try of native online cultural producers together with the platforms, intermediaries,
and fan communities operating interdependently, and disruptively, alongside leg-
acy media industries and across global media cultures” (Cunningham & Craig,
2019b: 1). Clearly, the phenomenon goes beyond entertainment and considers
information, education, external innovation of companies, and so on.
SME brings with it large quantities of innovation from which conventional
media are also feeding. This innovation is complex to understand since it is trans-
versal and aects the whole process of production and distribution (Krumsvik
etal., 2019).
In this new ecosystem, platforms exercise a strong control and there is a clear
decline of media business but there is also a great empowering of individuals and
considerable technological development, meaning that this system is a promising,
albeit unstable, one. In addition, the change has also come about with the arrival
of technological companies in the media sector and the entertainment industry for
example, with the merger of ATT and Discovery or Amazon purchasing the cata-
logue of MGM. One of the most important drivers of this change is the integra-
tion of media and life and tech-media content creation, aggregation and distribution
(Chan-Olmsted & Wang, 2019: 139).
The future for media companies is uncertain. They need managers with experi-
ence in dierent fields, media products segments and who are able to monetize the
collaboration with their audiences. The power of platforms may also change and the
status quo of media industries and entertainment industries may be altered.
Technology advances, entrepreneurship is constant, business models are changing
and users want to innovate to modify the ecosystem of communication and enter-
tainment. Technology could also change the landscape and threaten the establish-
ment of today’s operators (Smith & Telang, 2016). Many companies have disappeared,
24 José M. Álvarez-Monzoncillo and Marina Santín
many mergers have failed and small innovation projects have had the eect of break-
ing up. That has been the story of the last two decades. The pathway to success is
“the adoption of business model and strategies that utilize data, engage audiences,
and co-create value through content” (Chan-Olmsted & Wang, 2019: 144). The
power of UGCs is such that the sword of Heracles may influence the future of the
media and entertainment industries. Perhaps we are being naive, but we believe
there is still hope for the democratization of technology. There are new opportuni-
ties for old TV channels and newspapers, for the weakest cinema companies, but
they must work in a dierent way and with dierent targets. States’ public policies
also need to be redirected to favour quality in information and guarantee pluralism
and diversity at the same time.
2.3 Advertising and influencers: the dierent context of
platforms
The potential sector of UGCs and their capacity to distribute content, generate a
lot of visits and empathize with their followers has not gone unnoticed with the
media and brands. The advertising industry has undergone a great change due to
the upheaval caused by the Internet and the rise of UGCs. Classical ways of work-
ing have been destroyed although, by contrast, new opportunities have opened up
(Auletta, 2018). Independent artists from the creative sector have more options
than ever before to get themselves noticed. Having said that, they have to do it in
a scenario dominated by platforms and a loss of income in media industries. The
phenomenon of influencers shows us how the logic behind digital marketing and
the opportunities for creators, organizations and firms works.
All the opportunities for collaboration, as oered by social networks and other
spaces in which users share their opinion about dierent services or products, are
having an influence on digital marketing. Organizations are more and more keen
to increase the participation of their clients and build a greater engagement with
their brands. To do so, they seek to reap the maximum benefit from the opportu-
nities given to them by the net. Not only do they advertise in the dierent ways
available to them on social networks, but they also try to create communities of
like-minded people or fans, centered on the brand. They want these groups of
consumers to actively take part in boosting the sales and opportunities of each
brand. They even create emotional bonds with them. It is clear that users are
equipped to have an influence on brands on networking sites (Tajvidi et al., 2020)
and that is why brands are trying to improve their reputation with them and to
strengthen the links of the community.
Brands influence these communities which are organized on conventional
social networks so that people take part not only by commenting on content, but
also by sharing their experience of the brand and diminishing the critical com-
ments of unsatisfied customers. These followers are, at times, a source of inspira-
tion for the brands – so much so that we could even speak of an outsourcing of
creativity and innovation. In both directions, these followers become opinion
leaders who help enhance the reputation of brands. At the same time, the compa-
nies who use celebrities to promote their brand have also boosted the so-called
The marketing of UGC, media industries and business influence 25
“influencers”. These are people who have many followers and whose comments,
photos or videos can influence the marketing of products and services of dierent
brands, or improve their reputation against the competition. In the words of Enke
and Borchers (2019), the social media influencers (SMI) are “like third-party
actors who have established a significant number of relevant relationships with a
specific quality to and influence on organizational stakeholders through content
production, content distribution, interaction, and personal appearance on the
social web” (Enke & Borchers, 2019: 261).
The phenomenon and rise of influencers has been spectacular in recent years,
but, essentially, and from the point of view of marketing, their role is closely
related to what media companies used to do. The relationship between traditional
marketing and digital marketing is very close nowadays because they have some
common features, although they also have notable dierences too. The economy
which requires the analysis of cost per mile (CPM) in advertising campaigns has
been overcome by these new relationships which are created in communities of
followers. The advertising model has been turned on its head. SMIs can “cross
traditional boundaries in many ways and oscillate between intimacy and publicity,
authenticity and commercialization, ingratiation and critical distance … poten-
tially combine dierent roles, which, have traditionally been occupied by separate
actors” (Borchers, 2019: 255).
It is a challenge for organizations to know how to mix these functions in order
to improve their targets, since the eectiveness of increasing promotional expend-
iture has been proved beyond any shadow of doubt in virtually all countries.
Until recently, the conventional consumer was known thanks to the corporate
complexity made up of advertisers, advertising agencies, traditional media and
below the line strategies. Today, new consumer behavior in a scenario with new
social networks is more complex and requires more multidisciplinary empirical
information. Audiences have shifted from the role of content receivers to content
creators, distributors, and commentators. Consumers of one specific brand
“exhibit enhanced consumer loyalty, satisfaction, empowerment, connection,
emotional bonding, trust and commitment” (Brodie et al., 2013: 105). The change
is such that the fact that the advice may be mixed with endorsement in a way that
is indiscernible to the follower is being questioned and a regulation of the relation-
ship between the influencer and the follower may be considered to benefit follow-
ers (Mitchell, 2021).
Studies into SMIs and social media engagement with brands, organizations,
consumers in the area of marketing, advertising and public relations has been
important in recent years and that is reflected in some chapters of this book.
Research into the figure of the SMI has been analyzed from many enriching per-
spectives as revealed by dierent pieces of work (Abidin, 2016; Duy, 2017;
Khamis, Ang & Welling, 2017; Phua, Jin & Kim, 2017; Voorveld et al., 2018;
Himelboim & Golan, 2019; Jiménez-Castillo & Sánchez-Fernández, 2019; Jin,
Muqaddam & Ryu, 2019; Firat, 2019; Lee & Kim, 2020; Fumagalli, 2020;
Schouten, Janssen & Verspaget, 2020; Tuten, 2020; Guoquan et al., 2021).
The dierent approaches of the aforementioned studies facilitate an understand-
ing of how digital marketing is changing. The fact that consumers are, at the same
26 José M. Álvarez-Monzoncillo and Marina Santín
time, “prosumers” has altered the playing field of digital marketing. These
exchanges, for example, between guests (consumers) and hosts (providers) and
Airbnb has already been studied (Lang et al., 2020). In sharing economy platforms
(SEPs) the role of so-called “influencers” on social networks is key. The economic
growth of these platforms in the coming years is expected to be spectacular with
the coming together of artificial intelligence, big data and sharing economy
(Sundararajan, 2016; McAfee & Brynjolfsson, 2017; Srnicek, 2017; Nambisan,
Wright, Feldman, 2019; Van Dijck, Poell & De Waal, 2018; Van Dijk, 2020).
Digital marketing will build on these new advances to improve the strategic com-
munication of organizations.
The power of SMI has increased the commitment of the general public with
brands and influences the future behavior of its followers. Although it may seem
like a contradiction in terms, conflict and rivalry are one of the main reasons why
humans form groups centered on brands to exchange comments which are either
comical or hostile about the products of the competition. From that point of
view, identifying with a brand may generate hatred towards other brands (Ewing,
Wagsta & Powell, 2013; Ramírez, Veloutsou & Morgan-Thomas, 2019 and
Itani, 2020).
Companies often empower their consumers on the net so that they can take part
in the search for solutions for themselves or for other consumers of the same prod-
uct or service, thus enhancing even more the community eect of a brand (Hajli,
2018). There remains a lot to be studied about the workings of active and passive
participation of users on social networks and spaces on the net of shared economies
and how the actions of influencers generate possible eects related to information,
entertainment and money-making and these are issues which are being studied by
dierent authors (Dolan et al., 2019).
The act of sharing with a community is nothing new. In fact, it was the rev-
olution brought about by homo sapiens, according to Harari. However, the
term “sharing economy” has become very common due to its zero marginal cost
and collaborative commons (Rifkin, 2014). The general idea is that by sharing
between companies and user value is created (Tajvidi et al., 2017), even though
we cannot be 100 percent sure about consumers’ intentions when it comes to
the co-creation of value in SEPs. Some models have been set out to explain
these intentions using three approaches that oer the following concepts: social
support theory, consumer’s ethical perceptions and relationship quality theory
(Nadeem et al., 2020).
Even though the eectiveness of digital marketing on social networks has been
demonstrated, these networks also throw up risks for companies since the adver-
tising value of these spaces may be aected by many variables.
For example, there is great concern among the heads of marketing of companies
surrounding advertising on social networks and, in particular, YouTube: on the
one hand, there is great potential since interactive videos appear on platforms
where people spend a lot of time but, on the other, it is considered content of a
lower value as it is generated by users. For that reason, studies have already been
carried out to assess the eectiveness of advertising on YouTube, identifying fac-
tors aecting it in the context of online video advertising (Djafarova & Kramer,
The marketing of UGC, media industries and business influence 27
2019) or in the context of vlogs (Munnukka et al., 2019). Context is very impor-
tant in advertising and it is not enough to simply measure CPM.
A conventional TV advert is not the same as watching a video of your favourite
influencer or a video from a channel which you subscribe to, because you have a
specific interest, almost as if it were a hobby. As consumers are becoming more and
more omnivorous and nomadic, so, the place where they connect is a factor: away
from home, on public transport, in a waiting room, and so on. Quantitative met-
rics are not so good at measuring the influence of dierent types of advertising.
There are, in this hybrid environment, more and more emotional bonds, related to
reception. Brands want to develop ties with the community of fans and have great
interest in their comments and in the logic which leads them to share. There
remains work to be done in this area for the sector of academic research.
The credibility of influencers is a determining factor since it is hoped that they
will empathize with their followers and that their behavior will be ethical when
informing about or assessing brands rather than just being someone who is a
consumer of one product or another. There have been many studies which ana-
lyse the eectiveness of adverts from the discipline of marketing, but what is
missing is more analysis from the perspective of the communication of emotional
relationships between followers and influencers. In many cases, for example with
electronic products, users are influenced in their purchasing decision-making by
an expert influencer more than an attractive celebrity influencer (Trivedi &
Sama, 2020).
All that leads us to conclude that the eciency of influencers depends on their
level of empathy and the know-how of whoever is giving their opinion. In line
with that statement, there are studies which emphasize not only the relationship as
referred to the consumption of followers but also the relationship between influ-
encers and their followers (parasocial relationship) and the concept of organiza-
tional justice/fairness (Yuan & Lou, 2020).
The experience on each platform is unique and, from the point of view of
brands, not all social networks are equally ecient as far as reaching targets is
concerned. For that reason, there are authors (Voorveld et al., 2018) who state that
the concept of social networks is too wide to be studied without distinctions since
each platform has a unique advertising profile. “Engagement and advertising eval-
uations are related in a highly context-specific way because the relationship is
highly contingent on the platform” (Voorveld et al., 2018: 50). That implies that a
consumer´s engagement must also be assessed individually. Identifying and assess-
ing influencers requires a consideration not only of the content of messages but
also the context. Personality, style and the chosen platform will determine the
engagement of influencers and, consequently, its assessment from the point of view
of the influence business.
The possibilities oered to users by social networks such as YouTube or Instagram
for sharing, commenting on and assessing dierent content with a like/dislike facil-
itates the interaction, togetherness and sense of community. This boosts the value
of social networks, as, with certain profiles, it is easier for advertising and marketing
to identify where its target public may be. The diversity of contents about which
the user community interacts makes it possible to construct profiles, not only from
28 José M. Álvarez-Monzoncillo and Marina Santín
the point of view of entertainment, hobbies, journalistic interest, but also that of
brands. Digital platforms increase the value of the brand and are used on occasions
not only as just another promotional strategy but, at times, this space is its main
promotional strategy. The trend reveals, according to Schouten, Janssen & Verspaget
(2020), a shift of the patronage from celebrities to influencers. The stories, photo-
graphs and videos which they share, although their aim may be advertising, are not
necessarily seen as such by the consumer and that helps their persuasive power.
From the analysis of digital marketing and the role of influencers, the following
ideas are worth pointing out:
• That brands with a great reputation appear on social networks “has a positive
impact on message credibility, attitude toward the ad, purchase intention, and
eWOM intention” (Lee & Kim, 2020);
• “The influencer narratives impair the eectiveness of sponsorship disclosure
by analyzing the disclosure language in each post as well as the engagement
performances” (Feng, Chen & Kong, 2020:1);
• The identification of the desires and confidence by means of the relationship
between type of endorser and advertising eectiveness (Schouten, Janssen and
Verspaget, 2020);
• The negative spiral when it comes to sharing and commenting on content on
social networks is stronger than positiveness (Dhaoui & Webster, 2021);
• The business models evolve in a complex way, with a mixture and hybrid nature
for the types of monetization very similar to what is happening with Twitch
(Johnson & Woodcock, 2019; Sjöblom et al. 2019 and Diwanji et al., 2020);
• The popularity of the influencer has an influence on their leadership but if
their followers have very few accounts, this can have a negative eect on how
likeable they are (De Veirman, Cauberghe & Hudders, 2017);
• There is a trend to not take followers into account and to pay only for views
as Snapchat has announced;
• In advertising on social networks, the context triumphs over content and,
consequently, “content is not king” (Voorveld et al., 2018);
• “a high degree of congruence between the image of a social media influencer
and the consumer’s ideal self-image leads to eective endorsement outcomes”
(Shan, Chen & Lin, 2020: 590).
No-one doubts the potential of digital marketing in social networks, but there are
also problems derived from the fact that user data has become merchandise.
Understanding the term influencers in the broadest sense, we accept that the mar-
keting which uses it has a great future and will be integrated little by little into
traditional media and gradually advertising will shift towards UGC.
2.4 The risks of “platformization” and “datafication”
The network which was born from the social and supportive economy, at the same
time promising a democratization of the media, is in the hands of powerful global
companies which act in a market with little regulation, and not without accusations
The marketing of UGC, media industries and business influence 29
of monopolistic and scandalous practices such as that of Cambridge Analytics.
Benkler’s idea of the wealth of networks has given way to a savage new capitalism
with virtually no control. It is clear that the existing concern about the great global
power of platforms, which are go-betweens who reap the most benefit and the
total control of distribution without actually generating any content, is clear. This
question was dealt with in the above text where we talk about the great hydra
marketing information about users which was obtained during their experience.
The marketing of personal information on social networks happens a lot, but
the real danger in the control of this hydra is in the power that it exerts over pro-
duction, distribution and consumption of all types of content. Its power is not
limited to entertainment. Rather, its tentacles reach journalistic information. This
increasing power of the group over the value chain is having an influence on the
future of media industries, making media and cultural production. In addition,
personal information has become a commodity, a good to be traded. Platforms
have turned into a paradox by not truly facilitating social, cultural and socioeco-
nomic interaction (Gillespie, 2017). Its algorithms seek the maximum monetiza-
tion of these processes. Behind it all, there hides the power of intermediation and
the idea of a market characterized by “disappearing products” (Bilton, 2017).
Platforms are technological companies and not media ones, since they barely
finance content producers (Napoli & Caplan, 2017). However, they control an
industry which is becoming more and more important and represented by the
UGC or influencers. They have become the epicenter of digital marketing in as
much as they manage the contents created by users, the advertisers who want to
promote their products and services and the data of users who play an active role
on social networks.
The control of platforms is based on the process of datafication, taken as a “form
of mediation” (Powell, 2019). The reality is that true control does not lie in the
content but rather in big data. This great analysis of data is not neutral since it
aects our social relationships, our wishes and our reputation. Big data also creates
digital gaps. The management of data is aecting possible cultural experiences,
exposure to advertising and how we socialize depending on demographic and
sociocultural levels. It is also changing how we work, get information and enjoy
ourselves.
Platforms have also paved the way for new professions which are considered
“creative”: social media entertainers, influencers (Abidin, 2016) and many others
who work for free. They all adapt their work to platform algorithms in order to
monetize their work (Duy, Poell & Nieborg, 2019). Platformization renders cul-
tural commodities contingent (Nieborg & Poell, 2018). The social media plat-
forms’ business models are “increasingly more interdependent with creator culture”
(Cunningham & Craig, 2019b: 2). These authors also point out the internal con-
tradictions and tensions inherent in the main commercial platforms centered on
the value of content which was, originally, amateur.
Platforms are, apparently, neutral and distribute plural content in an attempt to
satisfy the demand for content by everybody; however they determine “the strat-
egies, routines, experiences, and expressions of creativity, labor, and citizenship
that shape cultural production through platforms” (Duy, Poell & Nieborg, 2019:
30 José M. Álvarez-Monzoncillo and Marina Santín
2). This side eect of platforms takes away from the cultural diversity and plural-
ism, all the more so in countries with very weak cultural industries which depend
on imports. Governments are already beginning to demand investment quotas and
the provision of a catalogue, as is the case with the European Union. This control
may bring with it many threats for diversity, pluralism or even democracy (Allocca,
2018 and Kyncl & Peyvan, 2017). Obviously, they are not “neutral carriers of
content” (Cunningham & Craig, 2019b), and, although it may seem paradoxical,
platforms aim their strategies more towards the spectator than the community (Van
Dijk, 2020). All of this is very dicult to analyse because algorithms work like a
black box which is mutating constantly.
This is not a debate between Utopians and Marxists or pessimists and optimists,
but platforms oer both sides of the coin: it is obvious that they are also providing
many people with opportunities. Platforms also promote the creation of content,
innovation, communication, the interests of groups of people, and so on. It has
made changes in creative work possible, and allowed many people to monetize
some of their passions and share them with others. We are not only talking about
the false equality of influencers with the female world linked to the stereotypes of
health and beauty, but many other influencers who comment on and assess the
news, give their opinion about products and services and, in particular, share
hobbies. There still remains a lot of work to be done in the area of marketing
forUGCs to be more active and improve their relationship with platforms and
audiences.
Despite the incredible control and power of platforms, we feel that the status
quo may change because of the high level of innovation of users and the technol-
ogy itself. The level of entrepreneurship and the constant appearance of new start-
ups are destabilizing the platforms’ power ecosystem. Governments are also
becoming aware of the need to regulate the process of social interaction via plat-
forms inasmuch as citizens’ rights are in play. There is considerable uncertainty but
the power of the hydra will not be eternal and absolute. Technological innovation
and the power of users counteracts it. New competitors will also appear, even from
among them. In addition, of course many pirates will serve to destabilize too. Let’s
hope that the voice of the people will be recognized not only by Alexa, Siri or
Cortana, or represented by their favourite influencers.
2.5 Pandemic narcissism and aspirational labor
Creative work has traditionally been considered very cool since it empowered the
workers of the cultural and creative industries. However, being an influencer, hav-
ing a lot of followers and making a lot of money in any of the social network
platforms is the “crème de la crème”. It portrays an image of modernness and
exclusivity with an aura of freedom and know-how beyond any shadow of doubt.
The subjects of the content created by influencers is very diverse, but the most
successful ones are gaming, gossip and beauty. The concept of happiness could be
studied, using the images of people on Instagram. If we add the varnish of entre-
preneurship, we are witnessing a phenomenon which is changing traditional
The marketing of UGC, media industries and business influence 31
marketing. In the same way as with influencers, their followers work together to
create a fake image of themselves. For that reason, it could be said that we are
living in a pandemic of narcissism (Twenge & Campbell, 2009). However, these
social networks distort our identities, empower status-seeking extremists, and ren-
der moderates all but invisible (Bail, 2021).
The problem is that there are many who are left behind, even those who mar-
ket their intimacy. In the same way as making media, there is a lot of precarious-
ness and too many regrettable situations. Many people use platforms as aspirational
labor – a job which has all the advantages and inconveniences of any other tem-
porary job (Duy, 2015). Undoubtedly, “the valorization of creative work nor-
malizes forms of precarious, individualized employment alongside a reduction in
workers’ rights and social protections” (Bishop, 2021:10). Precarious employ-
ment not only occurs in the area of media making but also on social media and
in the traditional employment of cultural industries (Curtin & Sanson, 2016 and
McRobbie, 2016).
It is obvious that few amateur users who post content on networks manage to
become professionals and make a good living. In addition, many of them end up
monetizing their intimacy. Their followers and the brands that sponsor them turn
into a boss with no regard for their rights: the algorithm of the platform. If things
do not work out, the boss recommends a change. So, for example, when the
content created by YouTube members is demonetized, they must change to adapt
to the algorithm strategy and recover income. Very often this situation may lead
to conflict between both parties (Caplan & Gillespie, 2020). That’s why YouTube
represents itself as “legislator, judge, and executive authority” (Kopf, 2020: 1). The
contemporary model of digital capitalism with the idea of accumulation of wealth
based on the exploitation of users is well known (Fuchs, 2014). Obviously, many
users find themselves in a position of inferiority and must direct their creativity
and content production to be in line with the principles of platforms. However,
many influencers in addition to reinterpreting the algorithm of Instagram, for
example, play at visibility, and influence the platforms in such a way that they have
a certain room for maneuver for “gaming the system” (Cotter, 2019). In other
words, “influencers play by the rules, but not always by the spirit of the rule”
(Cotter, 2019: 908).
The algorithm redesigns itself in favour of the interests of the platform and
companies. It learns, little by little, in order to optimize those interests. That is
known as “experience technologies” (Cotter & Reisdorf, 2020). This form of
learning uses many applications and also artificial intelligence (AI). At the same
time it produces a dynamic which destroys many dreams of the influencers them-
selves and their followers who tend to confuse reality with desire. All of that leads
to “value-laded algorithmic judgments map onto well-worn hierarchies of desira-
bility and employability that originate from systemic bias along the lines of class,
race, and gender” (Bishop, 2021: 1).
The creative process of influencers cannot be free as it must adapt to an algo-
rithm which is continually changing. There is a big brother who is watching all
the time in order to improve digital marketing: a job under observation, as Abidin
32 José M. Álvarez-Monzoncillo and Marina Santín
(2016) calls it. This permanent adaptation leads to a questioning of the false auton-
omy without commercial pressure and a fake collaborative experience of a com-
mercial community.
Many highly motivated users carry out unpaid work for personal reasons.
Indeed, “aspirational laborers expect that they will one day be compensated for
their productivity – be it through material rewards or social capital. But in the
meantime, they remain suspended in the consumption and promotion of branded
commodities” (Duy, 2017: 6). Behind the wishes of “self-branding” there lies a
situation of exploitation. The same as in real life, some make it and others don’t.
Social relevance and monetary income most of the time go hand in hand, but not
always. Many manage to reach a level of empowerment, doing a job and making a
living from something they find fulfilling.
Digital capitalism, which tends to maximize profits and increase share value, also
makes it possible for many influencers to do a job which is of interest to their
followers, although some profit is also made for platforms. They are the two sides
of the same coin: a few winners who work for their sponsors have a special rela-
tionship with their followers but there are many who try but don’t make it.
It is a phenomenon which has gone beyond entertainment, and the subjects it
deals with are often more relevant and useful for people looking for information
about health, learning languages or mathematics, or solving complex problems
such as what to do with a pension plan, and so on. The subjects are numerous and
varied, using video, podcast and text. They are UGCs whose activity combines
with that of influencers, which, basically, is that of a recommender of products.
This new phenomenon is not only changing digital marketing but also the ways in
which people receive information, training and have fun with a certain degree of
freedom.
2.6 Conclusion
There is a struggle to control the attention of users involving the media and the
content on oer created by both amateurs and professionals on social networks.
This level of competition paves the way for new business opportunities, leading
organizations to redefine their strategies and users to modify their consumption
styles in such a way that there has to be permanent innovation. This new informa-
tion and entertainment ecosystem has caused a large-scale fragmentation of audi-
ences and the apparition of diverse business models.
In the new scenario, the gathering of data about users’ experiences is of great
value for digital marketing and programmatic advertising. In this process, the
media lose presence in the life of the general public to the users who share content
on dierent platforms. This shift takes place in the context of rampant globaliza-
tion and retreat, and there is a certain sense of impotence among states regarding
the ability to regulate and reinforce the quality of information and guarantee
diversity, cultural pluralism and equal opportunities.
Audiences have become empowered, now that they can create all types of con-
tent and distribute it on dierent platforms. The sensation that all media industries
The marketing of UGC, media industries and business influence 33
and media making is going to be a business in which the process rather than the
product is controlled, is now appearing. It is the power of powerful platforms and
the context is more important than the content and may in fact be more lucrative.
Each platform is specializing in something and advertisers know the advantages
that each one oers. There is a clear dierence between, for example, Facebook,
YouTube or Instagram.
Vloggers, streamers, YouTubers, influencers, Instagramers, gameplayers,
TikTokers attract more and more of people’s time. Brands aim their advertising
towards those platforms by generating a feeling of community and certain emo-
tional bonds. Audiences are more and more fragmented and, paradoxically,
between fewer operators. New opportunities and success depend on control of
data, engagement and the co-creation of content.
However, platforms act like a hydra which mutates permanently and reinvents
itself constantly by means of algorithms, big data and artificial intelligence. It is
very ecient as it mechanizes many processes in order to market information
about users more eectively. These platforms have taken over by controlling the
intermediation between creators and they cannot be considered neutral since they
determine the type of content and influence the tastes of the general public. The
trend in the digital environment is towards a monopoly of the control of user data.
They have the capacity for feedback, almost all their innovation is outsourced and
they barely help creators. The latter are subject to the problems of aspirational
labor as is unpaid work, in the middle of a pandemic of global narcissism. However,
we feel that technological innovation, itself, entrepreneurship and the empower-
ment of people may be the sword of Heracles to finish with the overarching power
of the hydra. We are aware that states must also help to limit this power by guaran-
teeing many rights and improving democracy.
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DOI: 10.4324/9781003134176-4
3 The power of algorithms and
keysof participation
José Esteves
3.1 Introduction
We live in the era of algorithms. Some even called it a new economy, the algo-
rithm economy. They are the building blocks of any software application. Every
day we are increasingly exposed to algorithmically curated information. Algorithms
dictate everything users see online. From news to search information, to entertain-
ment (Netflix) and shopping (Amazon), algorithms increasingly impact how we
make decisions, how we consume information and how we understand the world
around us. Web users increasingly enjoy more access to information more speedily
and in an accessible manner as the media landscape shifts more towards digital and
mobile domains (Levordashka & Utz, 2016; Boczkowski et al., 2018). The general
belief that algorithmic use has a significant impact on daily life in this digital world
is reflected in the high level of interest in public and scholarly debates (Gillespie,
2014; Willson, 2017).
What is an algorithm? Essentially, algorithms are a step-by-step list of instruc-
tions that are executed, in a certain order, for solving a problem or performing a
task (Gillespie, 2014; Introna, 2016). For example, a cake recipe is an algorithm for
making a cake. We use algorithms every day. A computer program is an imple-
mented set of algorithms. Furthermore, algorithms are used to run the Internet
and all Web services, including Web searches.
The use of algorithms will continue to proliferate everywhere as huge amounts
of data are being generated, collected, analysed and managed by companies, public
administrations and governments. Additionally, the exponential growth of Internet
users makes it impossible to manage certain tasks manually. There are just not
enough moderators to thoroughly review each piece of content due to the volume
of content submitted on a daily basis. Moreover, the complexity and subtleties of
language present significant obstacles. Artificial Intelligence (AI) and techniques
like Machine Learning (ML), Deep Learning (DL) and Natural Language
Processing (NLP) are at the forefront of this new era of algorithms. Moreover,
AI-based algorithms, particularly the ones based on deep learning, are challenging
the notion of transparency and objectivity (Zerilli et al., 2018).
One of the Web services that uses intensive algorithms is social media. Social
media (and the Web) has grown exponentially in the last decade, and it has forever
changed the way we interact, work and do business (Sterrett et al., 2019). Social
40 José Esteves
media is defined as “a group of Internet-based applications that build on the ide-
ological and technological foundations of Web 2.0, and that allow the creation and
exchange of User Generated Content” (Kaplan & Haenlein, 2010, p. 61). It
includes, among other tools, weblogs (e.g. TechCrunch, Gary Vaynerchuk Blog,
and TMZ), microblogging (e.g. Twitter, Tumblr and Weibo), social networking
platforms (e.g. LinkedIn and Xing), platforms for sharing videos and images (e.g.
TikTok, YouTube, Dailymotion, Pinterest, and Instagram), instant messaging (e.g.
WhatsApp, Snapchat, WeChat and QQ), news aggregators (e.g. Reddit, Hacker
News and BizSugar) and social live streaming services (e.g. Socialive and Periscope).
All these social media services use algorithms to perform dierent tasks and dier-
ent personalization mechanisms. The adoption of social media algorithmic tools
serves as a helpful way to avoid becoming overwhelmed with the amount of infor-
mation. For example, Facebook and Twitter show to the user posts from his/her
closest friends and in the user feed because those are the people that the user interacts
with most often. However, it transfers human decision-making power towards social
media algorithms (Diakopoulos & Koliska, 2016). Often, users do not know how
these algorithms make decisions for them. The control of advertising that appears on
YouTube videos is an example. This is done automatically by algorithms that pick
which advertisements the user will see during a YouTube video in real time. These
decisions may have strong implications related to the risk of discriminating those
aected (Coeckelbergh, 2020; Cowgill & Tucker, 2020). This content bias issue and
the potential discrimination eect can occur intentionally (Speicher et al., 2018), or
even unintentionally through their respective choice of criteria (Diakopoulos, 2013).
Yet, there has been scant attention to the dierent eects of the social, organi-
zational, cultural and political dynamics related to the process of algorithm devel-
opment and implementation (Diakopoulos, 2016; Ettlinger, 2018). One of the big
issues is the potential risk of algorithmic bias and, in certain situations, ethical
concerns (Tsamados et al., 2021). Although the term bias diers based on the
context (Narayanan, 2018), in general, tt refers to errors that are systematic and
repeats that result in unfair consequences, like favouring one arbitrary user com-
munity over another.
This chapter outlines the challenges of using algorithms in social media, in
particular transparency and objectivity. First, I describe the main areas of algorithm
usage in social media. Then, I discuss the challenges associated with that use.
3.2 Social media and algorithms
As mentioned before, social media services are the kings of algorithm usage. This
section delves into the three most common uses of algorithms on social media:
content curation, user data collecting and content production.
3.2.1 Content curation
Curation is the act of selecting, classifying, filtering, prioritizing and presenting
content. Algorithmic curation and filtering is one of the most used tools on
social media (and digital media in general), and it is a great challenge in
The power of algorithms and keys of participation 41
hyper-technological and digital society (Thurman et al., 2019; Jussupow et al.,
2020). Essentially, filtering algorithms automatically select and filter data that the
user sees based on the extrapolation of viewing or usage preference of the user’s
previous behaviour, or many other users, to predict what that user might like too
(Messing & Westwood, 2014; Bakshy et al., 2015; Rader and Gray, 2015; Mothes
& Ohme, 2019). Algorithmic filtering has the potential to improve users’ experi-
ence (Bozdag, 2013; Ricci et al., 2015, Zarouali et al., 2021). Recommender
algorithms which are quite common in online retailing platforms like Amazon or
streaming platforms like Netflix are a type of curation algorithms.
A good example of algorithmic curation on social media is Facebook. In 2011,
algorithms were incorporated in the news feed feature. One of the key goals was
to decrease content hypersaturation on social media. Then, in 2018, the platform
decided to prioritize posts from family members and friends over public content.
According to Facebook, the company wants users to get the content they care
about and from the friends they care about. Most of social media companies argue
that the introduction of curation algorithms is to oer a user-centered solution for
managing vast amounts of content, allowing the users to quickly “see what mat-
ters” on their social feeds.
The three most frequently used filtering algorithms are content-based filtering,
demographic filtering and collaborative filtering (Ryngksai & Chameikho, 2014;
Thorat, 2015). Content-based filtering analyzes user preferences related to similar-
ities in products, services or content features to make recommendations for the
user. The demographic filtering approach employs the social-demographic data
(e.g. age, gender, job) of a user to select the content that might be suitable for
recommendation. Finally, collaborative filtering algorithms make suggestions for
an individual user by collecting data and preferences from many other users. This
perspective relies on the premise that social media users who accepted specific
content are likely to accept it again in the future.
There are dierent types of curation personalization approaches on social media.
The most common dierentiation is between explicit and implicit personalization
(Thurman & Schieres, 2012; Kaptein et al., 2015; Borgesius et al., 2016; Haim
et al., 2017; Yeung, 2018; Reviglio & Agosti, 2020). Explicit personalization relies
on data that the social media user proactively disclosed, and implicit personaliza-
tion uses data that the social media user has not directly volunteered; in some cases,
the user does not require a user to create an account. Basically, it tracks user behav-
iour. Some algorithms use a combination of both types of personalization. More
recently, a new type of personalization is emerging – contextual personalization–
which is based on the user’s current location and time.
Although curation algorithms have the potential to increase user experience and
satisfaction, this had not always been the result. In the last years, curation algo-
rithms have been criticized for creating “filter bubbles” and other phenomena on
social media (for a critical review see: Dahlgren, 2021; Cinelli et al., 2021). The
term filter bubbles, also called echo chambers (Sunstein, 2009) was coined by Eli
Pariser (2011), is related with the filtering algorithms mechanism that dictates the
information and opinions that the user can see based on the user’s own beliefs.
Some studies suggest that filter bubble state limits diversity of content, and it can
42 José Esteves
increase user polarization (Flaxman et al., 2016; Dubois & Blank, 2018; Chitra &
Musco, 2020). Some recent studies investigated whether the YouTube video rec-
ommendation system contributes to social media polarization by promoting sensi-
tive content (e.g. Hussein et al., 2020; Roth et al., 2020). Hussein et al. (2020)
discovered a filter bubble eect in top recommendations for all topics, excluding
vaccine controversy, after examining more than 56,000 videos across five topics.
They also found that for users with brand-new accounts, demographic factors like
gender, age, and geolocation have no eect on amplifying disinformation in
returned search results.
Despite the potential filter bubble risk, other studies do not support this view-
point (e.g., Hosanagar et al., 2014; Bakshy et al., 2015; Borgesius et al., 2016;
Fletcher and Nielsen, 2018; Boxell et al., 2020; Levy, 2021). A study conducted by
Hosanagar et al. (2014) found that algorithm filtering can build commonality, not
fragmentation, in online music preferences. Similar studies have found the YouTube
recommendation algorithm may foster the creation of highly homophilous com-
munities (e.g. Hussein et al., 2020; Kaiser & Rauchfleisch, 2020; Tang et al.,
2021). A recent study by Boxell et al. (2020) found that although aective polari-
zation has grown faster in the United States, it is decreasing in other countries with
high Internet usage. Also, the authors found that U.S. polarization was highest for
the older age groups (75+).
3.2.2 User data collection
Social media algorithms are also collecting vast amounts of data from social media
users, and using that data for dierent purposes, such as advertising, promotions,
business intelligence, data analytics, and personalization. As we mentioned before,
one of the most relevant aspects is how data collection tools aect how users find
social media content (Mittelstadt et al., 2016).
Social media firms collect enormous amounts of data about social media users,
allowing them to create advanced psychometric profiles of users (Schirch, 2021).
This data has proven to be immensely valuable for many social media firms because
their business model based on advertising revenues depends on this sort of data.
Social media data-centric business models focus on collecting as much data as
possible and link it to individual users, and create psychometric profiles of those
users which will let the firms better target their advertising strategies. This
data-centric approach aects the design of social media profiles, which is continu-
ously reviewed and updated.
Profile making has become omnipresent in social media platforms and digital
world. Users are frequently invited (most of the times it is mandatory) to create
profiles for using most of the Web services. Combining algorithms with profiling
approaches might be troublesome since the profiling criteria can provide contro-
versial categories and adverse eects, allowing discrimination or questionable user
targeting. For example, some years ago, Facebook allowed to target “anti-Semites”
(Angwin et al., 2017). Also, some social media platforms (e.g. Google, YouTube or
Facebook) allegedly used racial profiling (Angwin & Parris Jr., 2016; Gardner,
2020).
The power of algorithms and keys of participation 43
The rise of data analytics and emerging technologies like machine learning and
AI are expanding the ability of social media firms to contextualize data and draw
on insights gained from this data analysis. Overall, social media platforms based
their design, operations and business model on a datafication strategy. Social data
collected by social media firms can be categorized into four main categories:
• Personal data. It includes data that can be used to identify a person. Beyond
the common information like name, phone number, and addresses, it also
includes data of national IDs, passport number, social security numbers, gen-
der, photos, and financial data, as well as non-personally identifiable informa-
tion (Non-PII) such as Web browser type, Web browser cookies, language
preference, IP address, and device types and IDs.
• Engagement data. Also called interaction data, it includes data that describes
how consumers interact with the social media platforms, mobile apps, text
messages, emails, paid adverts.
• Behavioral data. It includes data generated or in response to the user experi-
ence with a social media platform and it encompasses the transactional details
such as product usage, purchase history, and gathering qualitative data (e.g.,
click tracking, scrolling, and mouse movement).
• Attitudinal data. It helps to understand that social media users think about the
platform and the content provided. Examples include online reviews, user
satisfaction surveys and content desirability.
3.2.3 Content creation
New emerging technologies are allowing social media firms, businesses and certain
users to create content automatically. Content creation automation is a growing
area, especially in the digital marketing arena. Certain AI tools like NLP are
quickly expanding content creation automation in social media (Farzindar
&Inkpen, 2017; Khan et al., 2020).
NLP, sometimes referred to as computational linguistics, is a branch of com-
puter science, AI and linguistics, that uses dierent machine learning techniques to
build algorithms that process and analyze natural language data. NLP comprises
two main areas: Natural Language Understanding (NLU) and Natural Language
Generation (NLG). While NLU concentrates on machine reading comprehension
through grammar and context, NLG concentrates on text generation.
The benefits of adopting NLP comprise the large volume of textual and speech
data that can be managed, the velocity of analysis (tens of thousands of documents
in seconds) and the capacity to detect tiny patterns that would be indecipherable
to human analysts otherwise. Further are the large number of data sets (known as
corpuses) and the time required to train some NLP models, and the challenge of
converting the NLP model analysis outcomes into a human-readable format are all
disadvantages.
Another example of algorithm application within content creation is the use of
“chatbots”, software programs based on AI to conduct online conversations via
audio or text (Shevat, 2017). Essentially, chatbots are algorithmically automated
44 José Esteves
users that are programmed to establish a conversation with a human agent (Følstad
& Brandtzæg, 2017; Prasetya et al., 2018). The main purposes for chatbots adop-
tion include informational support, social-emotional support or entertainment
(Gehl and Bakardjieva, 2017; Meng & Dai, 2021).
Also, chatbots have been investigated from several dimensions (Adamopoulou
and Moussiades, 2020). The most investigated dimension is related to the technical
aspects such as speech conversation systems (Masche & Le, 2018), and program-
ming techniques for chatbots (Long et al., 2019). Another relevant dimension is
related to human-chatbot interactions, for example chatbots and customer pur-
chase intentions (Luo et al., 2019), customer service and satisfaction (Kang & Kim,
2017; Chung et al., 2020) and collaboration and interaction with chatbots (Araújo
& Casais, 2020; De Cosmo et al., 2021; Li et al., 2021). The adoption of chatbots
is growing rapidly in instant messaging platforms and social media sites (e.g.
Kahiga, 2019; Assenmacher et al., 2020), and also some industries are at the fore-
front of chatbots adoption such as: healthcare (Safi et al., 2020), tourism (Melián-
González et al., 2019; Calvaresi et al., 2021; Li et al., 2021) and finance (Hwang
& Kim, 2021; Jang et al., 2021).
Social chatbots, also referred to as “emotional chatting machines” (Zhou et al.,
2018), are becoming popular. They are designed to be humanlike, with the poten-
tial to perceive, integrate, understand and express emotions (Stieglitz et al., 2017;
Zhou et al., 2018). Usually, users feel chatbots as friendly digital colleague (or even
co-worker) and not just as basic digital assistants (Costa, 2018; Ciechanowski et al.,
2019; Adamopoulou & Moussiades, 2020). Using an artificial conversational sys-
tem on Twitter, Xu et al. (2017) discovered that over 40 percent of user requests
are emotional rather than informative. Over time, chatbots have become more
sophisticated and based on new AI features such as sentiment analysis, machine
learning and deep learning, and they are able to detect emotional situations and
respond to the emotions appropriately during the conversation (Xu et al., 2017).
A big issue in content creation is the problem of fake content (Di Domenico
etal., 2021). Fake content is many times created and shared using social media algo-
rithms. In the case of fake news, this is especially crucial (Zimmer et al., 2019; Abu
Arqoub et al., 2020; Di Domenico & Visentin, 2020; Preston et al., 2021). Marx et
al. (2020) discovered that social bots interfered with COVID conversations on social
media, spread misinformation, and interspersed news from reputable sources.
The sharing habits of social media users are equally important. On Twitter,
tweets that include images and videos are more likely to be retweeted than only
text tweets (Goel et al., 2015; Vosoughi et al., 2018). Moreover, tweets with videos
are six times more likely to be retweeted than images (Farkas, 2016). For example,
during the 2016 United States Presidential election, tweets from Hillary Clinton
and Donald Trump with photos or videos obtained more favourites and retweets
than those without (Pancer & Poole, 2016; Lee & Xu, 2018). Vosoughi et al.
(2018) mention that real Twitter users are still 70 percent more likely to retweet
fake news than real news. In addition, the authors found that it is highly possible
that human users fake content than social media bots.
The problem is just starting and it is getting worse with the evolution of deep-
fake technology (Westerlund, 2019). This term is a combination of the words
The power of algorithms and keys of participation 45
“deep learning” (AI technique) and “fake” (not real). Deepfakes are AI-generated
content that are created by manipulating real-life images or videos of people to
create fictitious circumstances. (Ferreira et al., 2021). Deepfake videos by Barack
Obama and Mark Zuckerberg, for example, have gone viral on social media.
3.3 Algorithm challenges
If social media firms want to use algorithms in dierent tasks in social media, then
then they must confront the present challenges of using algorithms. Below, we
discuss some of the main challenges, particularly transparency and objectivity.
3.3.1 Transparency
The lack of transparency of social media algorithms is often cited by users (Burrell,
2016; Leetaru, 2018; Kim & Moon, 2021). Algorithmic solutions are becoming
progressively complex and heterogeneous, and substantially opaque. One of the
big concerns of social media algorithms is that they appear like a black box
(Pasquale, 2015; Burrell, 2016; Buhmann et al., 2020). Basically, there is an input,
something happens in the black box and an output comes out.
Some algorithms are designed to be deliberately opaque. In most of the cases
(with the exception of open-source platforms), it is nearly impossible to view the
internal algorithm operation. The reason is because most algorithms are proprie-
tary in nature (Kitchin, 2017). Some online firms are built around algorithms such
as online travel, search engines, social media and recommendation services, and
most of them are protected under intellectual property and patent regulations. As
more algorithm business models are emerging, more firms possess the incentive to
protect their algorithms like trade secrets, releasing minimal details but never fully
divulging the inner workings of their systems to the public. This opacity also
aects researchers and the analysis of transparency in algorithmic environments.
Some techniques like reverse-engineering help to study algorithms, but they are
complex and time-consuming (Kitchin, 2017).
Social media firms are sometimes open about what helps users and businesses to
understand how to improve their content rank and qualify as high-quality on these
social platforms. Consider the recent adjustments to Facebook’s algorithms: prior-
itize posts from friends that spark meaningful conversations and interactions over
transactions, and post more authentic and genuine video content. Recently, some
researchers have started proposing new mechanisms to improve algorithm aware-
ness. For example, Fouquaert and Mechant (2021) created the “Instawareness”
visual feedback tool to reduce illiteracy about the Instagram curation algorithm.
As the usage of algorithms increases, so does algorithmic illiteracy. An increasing
number of experts, researchers and institutions are requesting to teach algorithmic
literacy to Web users, especially younger generations. Sometimes, even AI experts
who build algorithms are unable to fully comprehend the machine learning tech-
niques that allow decisions to be made (Rainie & Anderson, 2017).
Another critical issue with transparency is not alerting the user that he or she is
communicating with a chatbot or that the information is being shared by a bot.
46 José Esteves
Bots created roughly 19 percent of all tweets connected to the 2016 United States
elections, according to a study by Bessi and Ferrara (2016). The combination of
technological advancement and usage of social cues bears the challenge for con-
sumers to accurately dierentiate between algorithmic or human conversational
agents. This development is highlighted by an empirical study on Google’s chatbot
Meena, in which its conversation quality was rated nearly as highly as the quality
of real human conversations, leaving previously appraised chatbots such as
Cleverbot or Mitsuku far behind (Adiwardana et al., 2020)
3.3.2 Objectivity
Algorithm adoption has frequently been questioned due to concerns about algo-
rithmic bias, objectivity and trustworthiness (Lee, 2018; Shin, 2021). To maintain