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Abstract

The social impact of research has usually been analysed through the scientific outcomes produced under the auspices of the research. The growth of scholarly content in social media and the use of altmetrics by researchers to track their work facilitate the advancement in evaluating the impact of research. However, there is a gap in the identification of evidence of the social impact in terms of what citizens are sharing on their social media platforms. This article applies a social impact in social media methodology (SISM) to identify quantitative and qualitative evidence of the potential or real social impact of research shared on social media, specifically on Twitter and Facebook. We define the social impact coverage ratio (SICOR) to identify the percentage of tweets and Facebook posts providing information about potential or actual social impact in relation to the total amount of social media data found related to specific research projects. We selected 10 projects in different fields of knowledge to calculate the SICOR, and the results indicate that 0.43% of the tweets and Facebook posts collected provide linkages with information about social impact. However, our analysis indicates that some projects have a high percentage (4.98%) and others have no evidence of social impact shared in social media. Examples of quantitative and qualitative evidence of social impact are provided to illustrate these results. A general finding is that novel evidences of social impact of research can be found in social media, becoming relevant platforms for scientists to spread quantitative and qualitative evidence of social impact in social media to capture the interest of citizens. Thus, social media users are showed to be intermediaries making visible and assessing evidence of social impact.
RESEARCH ARTICLE
Social impact in social media: A new method
to evaluate the social impact of research
Cristina M. Pulido
1
*, Gisela Redondo-Sama
2
, Teresa Sorde
´-Martı
´
3
, Ramon Flecha
4
1Department of Journalism and Communication Studies, Universitat Autonoma de Barcelona, Barcelona,
Spain, 2Department of Psychology and Sociology, Universidad de Zaragoza, Zaragoza, Spain,
3Department of Sociology, Universitat Autonoma de Barcelona, Barcelona, Spain, 4Department of
Sociology, Universitat de Barcelona (UB), Barcelona, Spain
*cristina.pulido@uab.cat
Abstract
The social impact of research has usually been analysed through the scientific outcomes
produced under the auspices of the research. The growth of scholarly content in social
media and the use of altmetrics by researchers to track their work facilitate the advancement
in evaluating the impact of research. However, there is a gap in the identification of evidence
of the social impact in terms of what citizens are sharing on their social media platforms.
This article applies a social impact in social media methodology (SISM) to identify quantita-
tive and qualitative evidence of the potential or real social impact of research shared on
social media, specifically on Twitter and Facebook. We define the social impact coverage
ratio (SICOR) to identify the percentage of tweets and Facebook posts providing information
about potential or actual social impact in relation to the total amount of social media data
found related to specific research projects. We selected 10 projects in different fields of
knowledge to calculate the SICOR, and the results indicate that 0.43% of the tweets and
Facebook posts collected provide linkages with information about social impact. However,
our analysis indicates that some projects have a high percentage (4.98%) and others have
no evidence of social impact shared in social media. Examples of quantitative and qualita-
tive evidence of social impact are provided to illustrate these results. A general finding is
that novel evidences of social impact of research can be found in social media, becoming
relevant platforms for scientists to spread quantitative and qualitative evidence of social
impact in social media to capture the interest of citizens. Thus, social media users are
showed to be intermediaries making visible and assessing evidence of social impact.
Introduction
The social impact of research is at the core of some of the debates influencing how scientists
develop their studies and how useful results for citizens and societies may be obtained. Con-
crete strategies to achieve social impact in particular research projects are related to a broader
understanding of the role of science in contemporary society. There is a need to explore dia-
logues between science and society not only to communicate and disseminate science but also
PLOS ONE | https://doi.org/10.1371/journal.pone.0203117 August 29, 2018 1 / 20
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OPEN ACCESS
Citation: Pulido CM, Redondo-Sama G, Sorde
´-
Martı
´T, Flecha R (2018) Social impact in social
media: A new method to evaluate the social impact
of research. PLoS ONE 13(8): e0203117. https://
doi.org/10.1371/journal.pone.0203117
Editor: Sergi Lozano, Institut Catalàde
Paleoecologia Humana i Evolucio
´Social (IPHES),
SPAIN
Received: November 8, 2017
Accepted: August 15, 2018
Published: August 29, 2018
Copyright: ©2018 Pulido et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: The research leading to these results has
received funding from the 7th Framework
Programme of the European Commission under
the Grant Agreement n˚ 613202 P.I. Ramon Flecha,
https://ec.europa.eu/research/fp7/index_en.cfm.
The funders had no role in study design, data
collection and analysis, decision to publish, or
preparation of the manuscript.
to achieve social improvements generated by science. Thus, the social impact of research
emerges as an increasing concern within the scientific community [1]. As Bornmann [2] said,
the assessment of this type of impact is badly needed and is more difficult than the measure-
ment of scientific impact; for this reason, it is urgent to advance in the methodologies and
approaches to measuring the social impact of research.
Several authors have approached the conceptualization of social impact, observing a lack of
generally accepted conceptual and instrumental frameworks [3]. It is common to find a wide
range of topics included in the contributions about social impact. In their analysis of the poli-
cies affecting land use, Hemling et al. [4] considered various domains in social impact, for
instance, agricultural employment or health risk. Moving to the field of flora and fauna, Wilder
and Walpole [5] studied the social impact of conservation projects, focusing on qualitative sto-
ries that provided information about changes in attitudes, behaviour, wellbeing and liveli-
hoods. In an extensive study by Godin and Dore [6], the authors provided an overview and
framework for the assessment of the contribution of science to society. They identified indica-
tors of the impact of science, mentioning some of the most relevant weaknesses and develop-
ing a typology of impact that includes eleven dimensions, with one of them being the impact
on society. The subdimensions of the impact of science on society focus on individuals (well-
being and quality of life, social implication and practices) and organizations (speeches, inter-
ventions and actions). For the authors, social impact “refers to the impact knowledge has on
welfare, and on the behaviours, practices and activities of people and groups” (p. 7).
In addition, the terms “social impact” and “societal impact” are sometimes used inter-
changeably. For instance, Bornmann [2] said that due to the difficulty of distinguishing social
benefits from the superior term of societal benefits, “in much literature the term ‘social impact’
is used instead of ‘societal impact’”(p. 218). However, in other cases, the distinction is made
[3], as in the present research. Similar to the definition used by the European Commission [7],
social impact is used to refer to economic impact, societal impact, environmental impact and,
additionally, human rights impact. Therefore, we use the term social impact as the broader
concept that includes social improvements in all the above mentioned areas obtained from the
transference of research results and representing positive steps towards the fulfilment of those
officially defined social goals, including the UN Sustainable Development Goals, the EU 2020
Agenda, or similar official targets. For instance, the Europe 2020 strategy defines five priority
targets with concrete indicators (employment, research and development, climate change and
energy, education and poverty and social exclusion) [8], and we consider the targets addressed
by objectives defined in the specific call that funds the research project.
This understanding of the social impact of research is connected to the creation of the
Social Impact Open Repository (SIOR), which constitutes the first open repository worldwide
that displays, cites and stores the social impact of research results [9]. The SIOR has linked to
ORCID and Wikipedia to allow the synergies of spreading information about the social impact
of research through diverse channels and audiences. It is relevant to mention that currently,
SIOR includes evidence of real social impact, which implies that the research results have led
to actual improvements in society. However, it is common to find evidence of potential social
impact in research projects. The potential social impact implies that in the development of the
research, there has been some evidence of the effectiveness of the research results in terms of
social impact, but the results have not yet been transferred.
Additionally, a common confusion is found among the uses of dissemination, transference
(policy impact) and social impact. While dissemination means to disseminate the knowledge
created by research to citizens, companies and institutions, transference refers to the use of
this knowledge by these different actors (or others), and finally, as already mentioned, social
impact refers to the actual improvements resulting from the use of this knowledge in relation
Social impact of research in social media
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Competing interests: The authors have declared
that no competing interests exist.
to the goals motivating the research project (such as the United Nations Sustainable Develop-
ment Goals). In the present research [3], it is argued that “social impact can be understood as
the culmination of the prior three stages of the research” (p.3). Therefore, this study builds on
previous contributions measuring the dissemination and transference of research and goes
beyond to propose a novel methodological approach to track social impact evidences.
In fact, the contribution that we develop in this article is based on the creation of a new
method to evaluate the evidence of social impact shared in social media. The evaluation pro-
posed is to measure the social impact coverage ratio (SICOR), focusing on the presence of evi-
dence of social impact shared in social media. Then, the article first presents some of the
contributions from the literature review focused on the research on social media as a source
for obtaining key data for monitoring or evaluating different research purposes. Second, the
SISM (social impact through social media) methodology[10] developed is introduced in detail.
This methodology identifies quantitative and qualitative evidence of the social impact of the
research shared on social media, specifically on Twitter and Facebook, and defines the SICOR,
the social impact coverage ratio. Next, the results are discussed, and lastly, the main conclu-
sions and further steps are presented.
Literature review
Social media research includes the analysis of citizens’ voices on a wide range of topics [11].
According to quantitative data from April 2017 published by Statista [12], Twitter and Face-
book are included in the top ten leading social networks worldwide, as ranked by the number
of active users. Facebook is at the top of the list, with 1,968 million active users, and Twitter
ranks 10
th
, with 319 million active users. Between them are the following social networks:
WhatsApp, YouTube, Facebook Messenger, WeChat, QQ, Instagram,Qzone and Tumblr. If
we look at altmetrics, the tracking of social networks for mentions of research outputs includes
Facebook, Twitter, Google+,LinkedIn, Sina Weibo and Pinterest. The social networks com-
mon to both sources are Facebook and Twitter. These are also popular platforms that have a
relevant coverage of scientific content and easy access to data, and therefore, the research proj-
ects selected here for application of the SISM methodology were chosen on these platforms.
Chew and Eysenbach [13] studied the presence of selected keywords in Twitter related to
public health issues, particularly during the 2009 H1N1 pandemic, identifying the potential for
health authorities to use social media to respond to the concerns and needs of society. Crooks
et al.[14] investigated Twitter activity in the context of a 5.8 magnitude earthquake in 2011 on
the East Coast of the United States, concluding that social media content can be useful for
event monitoring and can complement other sources of data to improve the understanding of
people’s responses to such events. Conversations among young Canadians posted on Facebook
and analysed by Martinello and Donelle [15] revealed housing and transportation as main
environmental concerns, and the project FoodRisc examined the role of social media to illus-
trate consumers’ quick responses during food crisis situations [16]. These types of contribu-
tions illustrate that social media research implies the understanding of citizens’ concerns in
different fields, including in relation to science.
Research on the synergies between science and citizens has increased over the years,
according to Fresco [17], and there is a growing interest among researchers and funding agen-
cies in how to facilitate communication channels to spread scientific results. For instance, in
1998, Lubchenco [18] advocated for a social contract that “represents a commitment on the
part of all scientists to devote their energies and talents to the most pressing problems of the
day, in proportion to their importance, in exchange for public funding”(p.491).
Social impact of research in social media
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In this framework, the recent debates on how to increase the impact of research have
acquired relevance in all fields of knowledge, and major developments address the methods
for measuring it. As highlighted by Feng Xia et al. [19], social media constitute an emerging
approach to evaluating the impact of scholarly publications, and it is relevant to consider the
influence of the journal, discipline, publication year and user type. The authors revealed that
people’s concerns differ by discipline and observed more interest in papers related to everyday
life, biology, and earth and environmental sciences. In the field of biomedical sciences, Haus-
tein et al. [20] analysed the dissemination of journal articles on Twitter to explore the correla-
tions between tweets and citations and proposed a framework to evaluate social media-based
metrics. In fact, different studies address the relationship between the presence of articles on
social networks and citations [21]. Bornmann [22] conducted a case study using a sample of
1,082 PLOS journal articles recommended in F1000 to explore the usefulness of altmetrics for
measuring the broader impact of research. The author presents evidence about Facebook and
Twitter as social networks that may indicate which papers in the biomedical sciences can be of
interest to broader audiences, not just to specialists in the area. One aspect of particular inter-
est resulting from this contribution is the potential to use altmetrics to measure the broader
impacts of research, including the societal impact. However, most of the studies investigating
social or societal impact lack a conceptualization underlying its measurement.
To the best of our knowledge, the assessment of social impact in social media (SISM) has
developed according to this gap. At the core of this study, we present and discuss the results
obtained through the application of the SICOR (social impact coverage ratio) with examples of
evidence of social impact shared in social media, particularly on Twitter and Facebook, and
the implications for further research.
Following these previous contributions, our research questions were as follows: Is there evi-
dence of social impact of research shared by citizens in social media? If so, is there quantitative
or qualitative evidence? How can social media contribute to identifying the social impact of
research?
Methods and data presentation
A group of new methodologies related to the analysis of online data has recently emerged. One
of these emerging methodologies is social media analytics [23], which was initially used most
in the marketing research field but also came to be used in other domains due to the multiple
possibilities opened up by the availability and richness of the data for different research pur-
poses. Likewise, the concern of how to evaluate the social impact of research as well as the
development of methodologies for addressing this concern has occupied central attention. The
development of SISM (Social Impact in Social Media) and the application of the SICOR (Social
Impact Coverage Ratio) is a contribution to advancement in the evaluation of the social impact
of research through the analysis of the social media selected (in this case, Twitter and Face-
book). Thus, SISM is novel in both social media analytics and among the methodologies used
to evaluate the social impact of research. This development has been made under IMPAC-
T-EV, a research project funded under the Framework Program FP7 of the Directorate-Gen-
eral for Research and Innovation of the European Commission. The main difference from
other methodologies for measuring the social impact of research is the disentanglement
between dissemination and social impact. While altmetrics is aimed at measuring research
results disseminated beyond academic and specialized spheres, SISM contribute to advancing
this measurement by shedding light on to what extent evidence of the social impact of research
is found in social media data. This involves the need to differentiate between tweets or Face-
book posts (Fb/posts) used to disseminate research findings from those used to share the social
Social impact of research in social media
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impact of research. We focus on the latter, investigating whether there is evidence of social
impact, including both potential and real social impact. In fact, the question is whether
research contributes and/or has the potential to contribute to improve the society or living
conditions considering one of these goals defined. What is the evidence? Next, we detail the
application of the methodology.
Data collection
To develop this study, the first step was to select research projects with social media data to be
analysed. The selection of research projects for application of the SISM methodology was per-
formed according to three criteria.
Criteria 1. Selection of success projects in FP7. The projects were success stories of the 7
th
Framework Programme (FP7) highlighted by the European Commission [24] in the fields of
knowledge of medicine, public health, biology and genomics. The FP7 published calls for proj-
ect proposals from 2007 to 2013. This implies that most of the projects funded in the last
period of the FP7 (2012 and 2013) are finalized or in the last phase of implementation.
Criteria 2. Period of implementation. We selected projects in the 2012–2013 period because
they combine recent research results with higher possibilities of having Twitter and Facebook
accounts compared with projects of previous years, as the presence of social accounts in
research increased over this period.
Criteria 3. Twitter and Facebook accounts. It was crucial that the selected projects had
active Twitter and Facebook accounts.
Table 1 summarizes the criteria and the final number of projects identified. As shown, 10
projects met the defined criteria. Projects in medical research and public health had higher
presence.
After the selection of projects, we defined the timeframe of social media data extraction on
Twitter and Facebook from the starting date of the project until the day of the search, as pre-
sented in Table 2.
The second step was to define the search strategies for extracting social media data related
to the research projects selected. In this line, we defined three search strategies.
Strategy 1. To extract messages published on the Twitter account and the Facebook page of
the selected projects. We listed the Twitter accounts and Facebook pages related to each proj-
ect in order to look at the available information. In this case, it is important to clarify that the
tweets published under the corresponding Twitter project account are original tweets or
retweets made from this account. It is relevant to mention that in one case, the Twitter account
and Facebook page were linked to the website of the research group leading the project. In this
case, we selected tweets and Facebook posts related to the project. For instance, in the case of
the Twitter account, the research group created a specific hashtag to publish messages related
to the project; therefore, we selected only the tweets published under this hashtag. In the analy-
sis, we prioritized the analysis of the tweets and Facebook posts that received some type of
Table 1. Selection criteria and number of projects.
Field of
Knowledge
Criteria 1. Success stories
FP7
Criteria 2. Starting year 2012 &
2013
Criteria 3. Twitter and
Facebook
Medical research 98 11 3
Public Health 37 11 4
Genomics 14 2 1
Biology 9 2 2
TOTAL 158 26 10
https://doi.org/10.1371/journal.pone.0203117.t001
Social impact of research in social media
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interaction (likes, retweets or shares) because such interaction is a proxy for citizens’ interest.
In doing so, we used the R program and NVivoto extract the data and proceed with the analy-
sis. Once we obtained the data from Twitter and Facebook, we were able to have an overview
of the information to be further analysed, as shown in Table 3.
We focused the second and third strategies on Twitter data. In both strategies, we extracted
Twitter data directly from the Twitter Advanced Search tool, as the API connected to NVivo
and the R program covers only a specific period of time limited to 7/9 days. Therefore, the use
of the Twitter Advanced Search tool made it possible to obtain historic data without a period
limitation. We downloaded the results in PDF and then uploaded them to NVivo.
Strategy 2. To use the project acronym combined with other keywords, such as FP7 or EU.
This strategy made it possible to obtain tweets mentioning the project. Table 4 presents the
number of tweets obtained with this strategy.
Strategy 3. To use searchable research results of projects to obtain Twitter data. We defined
a list of research results, one for each project, and converted them into keywords. We selected
one searchable keyword for each project from its website or other relevant sources, for
instance, the brief presentations prepared by the European Commission and published in
CORDIS. Once we had the searchable research results, we used the Twitter Advanced Search
tool to obtain tweets, as presented in Table 5.
The sum of the data obtained from these three strategies allowed us to obtain a total of
3,425 tweets and 1,925 posts on public Facebook pages. Table 6 presents a summary of the
results.
Table 2. Timeframe for the extraction of Twitter and Facebook data.
Project Period Months
Project 1 From 2012-11-01 to 2017-04-25 54.53
Project 2 From 2012-11-01 to 2017-04-25 54.53
Project 3 From 2013-01-01 to 2017-04-25 52.50
Project 4 From 2013-10-01 to 2017-04-25 43.40
Project 5 From 2013-11-01 to 2017-04-25 42.37
Project 6 From 2013-02-01 to 2017-04-25 51.47
Project 7 From 2013-11-01 to 2017-04-25 42.37
Project 8 From 2012-11-01 to 2017-04-25 54.54
Project 9 From 2012-11-01 to 2017-04-25 54.54
Project 10 From 2012-08-01 to 2017-04-25 57.60
https://doi.org/10.1371/journal.pone.0203117.t002
Table 3. Tweets and Facebook posts per project obtained–Strategy 1.
Project Tweets Facebook posts
Project 1 952 585
Project 2 403 423
Project 3 896 396
Project 4 21 41
Project 5 410 16
Project 6 124 74
Project 7 148 64
Project 8 56 236
Project 9 55 43
Project 10 106 47
TOTAL 3,171 1,925
https://doi.org/10.1371/journal.pone.0203117.t003
Social impact of research in social media
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We imported the data obtained from the three search strategies into NVivo to analyse.
Next, we select tweets and Facebook posts providing linkages with quantitative or qualitative
evidence of social impact, and we complied with the terms of service for the social media from
which the data were collected. By quantitative and qualitative evidence, we mean data or infor-
mation that shows how the implementation of research results has led to improvements
towards the fulfilment of the objectives defined in the EU2020 strategy of the European Com-
mission or other official targets. For instance, in the case of quantitative evidence, we searched
tweets and Facebook posts providing linkages with quantitative information about improve-
ments obtained through the implementation of the research results of the project. In relation
to qualitative evidence, for example, we searched for testimonies that show a positive evalua-
tion of the improvement due to the implementation of research results. In relation to this step,
it is important to highlight that social media users are intermediaries making visible evidence
of social impact. Users often share evidence, sometimes sharing a link to an external resource
(e.g., a video, an official report, a scientific article, news published on media). We identified
evidence of social impact in these sources.
Data analysis
We analysed all tweets and Facebook posts collected (3,425 tweets and 1,925 Facebook posts)
to calculate the ratio of social media data with evidence of social impact in relation to the total
Table 4. Tweets obtained per project–Strategy 2.
Project Tweets
Project 1 10
Project 2 0
Project 3 2
Project 4 5
Project 5 4
Project 6 175
Project 7 4
Project 8 5
Project 9 4
Project 10 17
TOTAL 226
https://doi.org/10.1371/journal.pone.0203117.t004
Table 5. List of searchable research results–Strategy 3.
Project Searchable Research result Tweets
Project 1 MACSQuant1Tyto 3
Project 2 Prototype screening tests for pre-eclampsia 0
Project 3 Early Life Exposome 5
Project 4 Splendid system 0
Project 5 EuroFIT programme 4
Project 6 Fishchoice tool 3
Project 7 Vitamin D-enhanced eggs 5
Project 8 Developakure clinical trials 4
Project 9 Precision Livestock Farming Applications 3
Project 10 NOSHAN technologies 1
TOTAL 28
https://doi.org/10.1371/journal.pone.0203117.t005
Social impact of research in social media
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amount of social media data extracted from the research projects selected. The aim was to
answer the question whether or not there is evidence of social impact shared by citizens in
social media. Once we had the tweets and Facebook posts selected for each project, we identi-
fied the number of tweets and Facebook posts responding or not to the criteria of presenting
evidence of the social impact of research. In the final step of this search, we defined a ratio of
coverage adapted to this calculation called the SICOR, the social impact coverage ratio:
SICOR ¼Pn
i¼1gi
Pn
i¼1Ti¼g1þg2þ. . . þgn
T1þT2þ. . . þTn
where:
γ
i
is the total number of messages obtained about project iwith evidence of social impact
on social media platforms (Twitter, Facebook, Instagram, etc.);
T
i
is the total number of messages from project ion social media platforms (Twitter, Face-
book, Instagram, etc.); and
nis the number of projects selected.
The result is expressed in percentages. In this paper, we use the SICOR for Twitter and
Facebook thus:
g2 fTw;Fbg
and
T2 fTw;Fbg:
Analytical categories and codebook
The researchers who carried out the analysis of the social media data collected are specialists in
the social impact of research and research on social media. Before conducting the full analysis,
two aspects were guaranteed. First, how to identify evidence of social impact relating to the tar-
gets defined by the EU2020 strategy or to specific goals defined by the call addressed was clari-
fied. Second, we held a pilot to test the methodology with one research project that we know
has led to considerable social impact, which allowed us to clarify whether or not it was possible
to detect evidence of social impact shared in social media. Once the pilot showed positive
results, the next step was to extend the analysis to another set of projects and finally to the
whole sample. The construction of the analytical categories was defined a priori, revised
accordingly and lastly applied to the full sample.
Table 6. Summary of the Twitter and Facebook data collected.
Project Tweets Facebook posts
Project 1 965 585
Project 2 403 423
Project 3 903 396
Project 4 26 41
Project 5 418 16
Project 6 302 74
Project 7 157 64
Project 8 65 236
Project 9 62 43
Project 10 124 47
TOTAL 3,425 1,925
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Different observations should be made. First, in this previous analysis, we found that the
tweets and Facebook users play a key role as “intermediaries,” serving as bridges between the
larger public and the evidence of social impact. Social media users usually share a quote or par-
agraph introducing evidence of social impact and/or link to an external resource, for instance,
a video, official report, scientific article, news story published on media, etc., where evidence of
the social impact is available. This fact has implications for our study, as our unit of analysis is
all the information included in the tweets or Facebook posts. This means that our analysis
reaches the external resources linked to find evidence of social impact, and for this reason, we
defined tweets or Facebook posts providing linkages with information about social impact.
Second, the other important aspect is the analysis of the users’ profile descriptions, which
requires much more development in future research given the existing limitations. For
instance, some profiles are users’ restricted due to privacy reasons, so the information is not
available; other accounts have only the name of the user with no description of their profile
available. Therefore, we gave priority to the identification of evidence of social impact includ-
ing whether a post obtained interaction (retweets, likes or shares) or was published on
accounts other than that of the research project itself. In the case of the profile analysis, we
added only an exploratory preliminary result because this requires further development. Con-
sidering all these previous details, the codebook (see Table 7) that we present as follows is a
result of this previous research.
How to analyse Twitter and Facebook data
To illustrate how we analysed data from Twitter and Facebook, we provide one example of
each type of evidence of social impact defined, considering both real and potential social
impact, with the type of interaction obtained and the profiles of those who have interacted.
QUANESISM. Tweet by ZeroHunger Challenge @ZeroHunger published on 3 May 2016.
Text: How re-using food waste for animal feed cuts carbon emissions.-NOSHAN project hubs.
ly/H02SmrP0. 7 retweets and 5 likes.
The unit of analysis is all the content of the tweet, including the external link. If we limited
our analysis to the tweet itself, it would not be evidence. Examining the external link is neces-
sary to find whether there is evidence of social impact. The aim of this project was to investi-
gate the process and technologies needed to use food waste for feed production at low cost,
with low energy consumption and with a maximal evaluation of the starting wastes. This tweet
provides a link to news published in the PHYS.org portal [25], which specializes in science
Table 7. Codebook of SISM.
CODE Element Definition
ESISM Evidence of social impact shared in
social media
Evidence of social impact is a research result that contributes to the achievement of a particular objective of the
society defined by the corresponding institution,in this case, one of the targets addressed in the EU2020 strategy
or the target addressed in the call of the funding project. Evidence can be of potential or already achieved social
impact.
QUALESISM Qualitative evidence of social
impact
The evidence provided gives qualitative information about improvements obtained through the implementation
of the research results of the project linked to the one of the targets of the EU2020 strategy or the target addressed
in the call of the funding project. Evidence can be of potential or already achieved social impact.
QUANESISM Quantitative evidence of social
impact
The evidence provided gives quantitative information about improvements obtained through the implementation
of the research results of the project linked to the one of the targets of the EU2020 strategy or the target addressed
in the call of the funding project. Evidence can be of potential or already achieved social impact.
INTER Interaction of the tweet or Fb post The tweet or post has been shared, liked retweeted or published by an account other than the project account
itself.
PROF D Diverse profiles Diverse profiles of citizens have interacted with the tweet or Fb post.
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news. The news story includes an interview with the main researcher that provides the follow-
ing quotation with quantitative evidence:
’Our results demonstrated that with a NOSHAN 10 percent mix diet, for every kilogram of
broiler chicken feed, carbon dioxide emissions were reduced by 0.3 kg compared to a non-
food waste diet,’ explains Montse Jorba, NOSHAN project coordinator. ’If 1 percent of total
chicken broiler feed in Europe was switched to the 10 percent NOSHAN mix diet, the total
amount of CO2 emissions avoided would be 0.62 million tons each year.’[25]
This quantitative evidence “a NOSHAN 10 percent mix diet, for every kilogram of broiler
chicken feed, carbon dioxide emissions carbon dioxide emissions were reduced by 0.3 kg to a
non-food waste diet” is linked directly with the Europe 2020 target of Climate Change &
Energy, specifically with the target of reducing greenhouse gas emissions by 20% compared to
the levels in 1990 [8]. The illustrative extrapolation the coordinator mentioned in the news is
also an example of quantitative evidence, although is an extrapolation based on the specific
research result.
This tweet was captured by the Acronym search strategy. It is a message tweeted by an
account that is not related to the research project. The twitter account is that of the Zero Hun-
ger Challenge movement, which supports the goals of the UN. The interaction obtained is 7
retweets and 5 likes. Regarding the profiles of those who retweeted and clicked “like”, there
were activists, a journalist, an eco-friendly citizen, a global news service, restricted profiles (no
information is available on those who have retweeted) and one account with no information
in its profile.
The following example illustrates the analysis of QUALESISM: Tweet by @eurofitFP7 pub-
lished on4 October 2016. Text: See our great new EuroFIT video on youtube! https://t.co/
TocQwMiW3c 9 retweets and 5 likes.
The aim of this project is to improve health through the implementation of two novel tech-
nologies to achieve a healthier lifestyle. The tweet provides a link to a video on YouTube on
the project’s results. In this video, we found qualitative evidence from people who tested the
EuroFit programme; there are quotes from men who said that they have experienced improved
health results using this method and that they are more aware of how to manage their health:
One end-user said: I have really amazing results from the start, because I managed to
change a lot of things in my life. And other one: I was more conscious of what I ate, I was
more conscious of taking more steps throughout the day and also standing up a little more.
[26]
The research applies the well researched scientific evidence to the management of health
issues in daily life. The video presents the research but also includes a section where end-users
talk about the health improvements they experienced. The quotes extracted are some examples
of the testimonies collected. All agree that they have improved their health and learned healthy
habits for their daily lives. These are examples of qualitative evidence linked with the target of
the call HEALTH.2013.3.3–1—Social innovation for health promotion [27] that has the objec-
tives of reducing sedentary habits in the population and promoting healthy habits. This
research contributes to this target, as we see in the video testimonies. Regarding the interaction
obtained, this tweet achieved 9 retweets and 5 likes. In this case, the profiles of the interacting
citizens show involvement in sport issues, including sport trainers, sport enthusiasts and some
researchers.
Social impact of research in social media
PLOS ONE | https://doi.org/10.1371/journal.pone.0203117 August 29, 2018 10 / 20
To summarize the analysis, in Table 8 below, we provide a summary with examples illus-
trating the evidence found.
Quantitative evidence of social impact in social media
There is a greater presence of tweets/Fb posts with quantitative evidence (14) than with quali-
tative evidence (9) in the total number of tweets/Fb posts identified with evidence of social
impact. Most of the tweets/Fb posts with quantitative evidence of social impact are from scien-
tific articles published in peer-reviewed international journals and show potential social
impact. In Table 8, we introduce 3 examples of this type of tweets/Fb posts with quantitative
evidence:
The first tweet with quantitative social impact selected is from project 7. The aim of this
project was to provide high-quality scientific evidence for preventing vitamin D deficiency in
European citizens. The tweet highlighted the main contribution of the published study, that is,
“Weekly consumption of 7 vitamin D-enhanced eggs has an important impact on winter vita-
min D status in adults” [28]. The quantitative evidence shared in social media was extracted
from a news publication in a blog on health news. This blog collects scientific articles of
research results. In this case, the blog disseminated the research result focused on how vitamin
D-enhanced eggs improve vitamin D deficiency in wintertime, with the published results
obtained by the research team of the project selected. The quantitative evidence illustrates that
the group of adults who consumed vitamin D-enhanced eggs did not suffer from vitamin D
deficiency, as opposed to the control group, which showed a significant decrease in vitamin D
over the winter. The specific evidence is the following extracted from the article [28]:
With the use of a within-group analysis, it was shown that, although serum 25(OH) D in
the control group significantly decreased over winter (mean ±SD: -6.4 ±6.7 nmol/L;
P = 0.001), there was no change in the 2 groups who consumed vitamin D-enhanced eggs
(P>0.1 for both. (p. 629)
This evidence contributes to achievement of the target defined in the call addressed that is
KBBE.2013.2.2–03—Food-based solutions for the eradication of vitamin D deficiency and
health promotion throughout the life cycle [29]. The quantitative evidence shows how the con-
sumption of vitamin D-enhanced eggs reduces vitamin D deficiency.
The second example of this table corresponds to the example of quantitative evidence of
social impact provided in the previous section.
The third example is a Facebook post from project 3 that is also tweeted. Therefore, this evi-
dence was published in both social media sources analysed. The aim of this project was to
Table 8. Examples of tweets and Facebook posts with quantitative evidence of social impact.
Tweet/ Fb post Project
Weekly consumption of 7 vitamin D-enhanced eggs has an important impact on winter vitamin D status
in adults. http://www.greenmedinfo.com/article/weekly consumption-7-vitamin-d-enhanced-eggs-has-
important-impact-winter-vitam
Project 7
How re-using food waste for animal feed cuts carbon emissions NOSHAN Project: http://hubs.ly/
H02SmrP0
Project
10
Here’s a HELIX publication for you!
Assessment of metabolic phenotypic variability in children’s urine using 1H NMR spectroscopy.—
PubMed—NCBI
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5395814/
Project 3
https://doi.org/10.1371/journal.pone.0203117.t008
Social impact of research in social media
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measure a range of chemical and physical environmental hazards in food, consumer products,
water, air, noise, and the built environment in the pre- and postnatal early-life periods. This
Facebook post and tweet links directly to a scientific article [30] that shows the precision of the
spectroscopic platform:
Using 1H NMR spectroscopy we characterized short-term variability in urinary metabolites
measured from 20 children aged 8–9 years old. Daily spot morning, night-time and pooled
(50:50 morning and night-time) urine samples across six days (18 samples per child) were
analysed, and 44 metabolites quantified. Intraclass correlation coefficients (ICC) and mixed
effect models were applied to assess the reproducibility and biological variance of metabolic
phenotypes. Excellent analytical reproducibility and precision was demonstrated for the 1H
NMR spectroscopic platform (median CV 7.2%)
.
(p.1)
This evidence is linked to the target defined in the call “ENV.2012.6.4–3—Integrating envi-
ronmental and health data to advance knowledge of the role of environment in human health
and well-being in support of a European exposome initiative” [31]. The evidence provided
shows how the project’s results have contributed to building technology for improving the
data collection to advance in the knowledge of the role of the environment in human health,
especially in early life. The interaction obtained is one retweet from a citizen from Nigeria
interested in health issues, according to the information available in his profile.
Qualitative evidence of social impact in social media
We found qualitative evidence of the social impact of different projects, as shown in Table 9.
Similarly to the quantitative evidence, the qualitative cases also demonstrate potential social
impact. The three examples provided have in common that they are tweets or Facebook posts
that link to videos where the end users of the research project explain their improvements
once they have implemented the research results.
The first tweet with qualitative evidence selected is from project 4. The aim of this project is
to produce a system that helps in the prevention of obesity and eating disorders, targeting
young people and adults [32]. The twitter account that published this tweet is that of the Future
and Emerging Technologies Programme of the European Commission, and a link to a Euro-
news video is provided. This video shows how the patients using the technology developed in
the research achieved control of their eating disorders, through the testimonies of patients
commenting on the positive results they have obtained. These testimonies are included in the
news article that complements the video. An example of these testimonies is as follows:
Pierre Vial has lost 43 kilos over the past nine and a half months. He and other patients at
the eating disorder clinic explain the effects obesity and anorexia have had on their lives.
Table 9. Examples of tweets and Facebook posts with qualitative evidence of social impact.
Tweet/ Fb post Project
’#Tech trialled in fight against ticking #obesity timebomb’ #H2O20 #SPLENDID project, by @euronews
http://www.euronews.com/2016/06/17/technology-trialled-in-fight-against-ticking-timebomb-of-
obesity/ . . . via @eu_ehealth
Project 4
EU-PLF and Fancom b.v. in the news again. This time in Euronews!
http://www.euronews.com/2016/05/09/big-farmer-is-watching-surveillance-technology-monitors-
animal-wellbeing
Project 9
See our great new EuroFIT video on youtube!
https://www.youtube.com/watch?v=CHkbnD8IgZw&feature=youtu.be
Project 5
https://doi.org/10.1371/journal.pone.0203117.t009
Social impact of research in social media
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Another patient, Karin Borell, still has some months to go at the clinic but, after decades of
battling anorexia, is beginning to be able to visualise life without the illness: “On a good day
I see myself living a normal life without an eating disorder, without problems with food.
That’s really all I wish right now”.[32]
This qualitative evidence shows how the research results contribute to the achievement of
the target goals of the call addressed:“ICT-2013.5.1—Personalised health, active ageing, and
independent living”. [33] In this case, the results are robust, particularly for people suffering
chronic diseases and desiring to improve their health; people who have applied the research
findings are improving their eating disorders and better managing their health. The value of
this evidence is the inclusion of the patients’ voices stating the impact of the research results on
their health.
The second example is a Facebook post from project 9, which provides a link to a Euronews
video. The aim of this project is to bring some tools from the lab to the farm in order to guar-
antee a better management of the farm and animal welfare. In this video [34], there are quotes
from farmers using the new system developed through the research results of the project.
These quotes show how use of the new system is improving the management of the farm and
the health of the animals; some examples are provided:
Cameras and microphones help me detect in real time when the animals are stressed for
whatever reason,” explained farmer Twan Colberts. “So I can find solutions faster and in
more efficient ways, without me being constantly here, checking each animal.”
This evidence shows how the research results contribute to addressing the objectives speci-
fied in the call “KBBE.2012.1.1–02—Animal and farm-centric approach to precision livestock
farming in Europe” [29], particularly, to improve the precision of livestock farming in Europe.
The interaction obtained is composed of6 likes and 1 share. The profiles are diverse, but some
of them do not disclose personal information; others have not added a profile description, and
only their name and photo are available.
Interrater reliability (kappa)
The analysis of tweets and Facebook posts providing linkages with information about
social impact was conducted following a content analysis method in which reliability was
based on a peer review process. This sample is composed of 3,425 tweets and 1,925 Fb/
posts. Each tweet and Facebook post was analysed to identify whether or not it contains
evidence of social impact. Each researcher has the codebook a priori. We used interrater
reliability in examining the agreement between the two raters on the assignment of the
categories defined through Cohen’s kappa. We used SPSS to calculate this coefficient. We
exported an excel sheet with the sample coded by the two researchers being 1 (is evidence
of social impact, either potential or real) and 0 (is not evidence of social impact) to SPSS.
The cases where agreement was not achieved were not considered as containing evidence
of social impact. The result obtained is 0.979; considering the interpretation of this num-
ber according to Landis & Koch [35], our level of agreement is almost perfect, and thus,
our analysis is reliable. To sum up the data analysis, the description of the steps followed
is explained:
Step 1. Data analysis I. We included all data collected in an excel sheet to proceed with the
analysis. Prior to the analysis, researchers read the codebook to keep in mind the information
that should be identified.
Social impact of research in social media
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Step 2. Each researcher involved reviewed case by case the tweets and Facebook posts to
identify whether they provide links with evidence of social impact or not. If the researcher con-
siders there to be evidence of social impact, he or she introduces the value of 1into the column,
and if not, the value of 0.
Step 3. Once all the researchers have finished this step, the next step is to export the excel
sheet to SPSS to extract the kappa coefficient.
Step 4. Data Analysis II. The following step was to analyse case by case the tweets and Face-
book posts identified as providing linkages with information of social impact and classify them
as quantitative or qualitative evidence of social impact.
Step 5. The interaction received was analysed because this determines to which extent this
evidence of social impact has captured the attention of citizens (in the form of how many likes,
shares, or retweets the post has).
Step 6. Finally, if available, the profile descriptions of the citizens interacting through
retweeting or sharing the Facebook post were considered.
Step 7. SICOR was calculated. It could be applied to the complete sample (all data projects)
or to each project, as we will see in the next section.
Results
The total number of tweets and Fb/posts collected from the 10 projects is 5,350. After the con-
tent analysis, we identified 23 tweets and Facebook posts providing linkages to information
about social impact. To respond to the research question, which considered whether there is
evidence of social impact shared by citizens in social media, the answer was affirmative,
although the coverage ratio is low. Both Twitter and Facebook users retweeted or shared evi-
dence of social impact, and therefore, these two social media networks are valid sources for
expanding knowledge on the assessment of social impact. Table 10 shows the social impact
coverage ratio in relation to the total number of messages analysed.
The analysis of each of the projects selected revealed some results to consider. Of the 10
projects, 7 had evidence, but those projects did not necessarily have more Tweets and Face-
book posts. In fact, some projects with fewer than 70 tweets and 50 Facebook posts have more
evidence of social impact than other projects with more than 400 tweets and 400 Facebook
posts. This result indicates that the number of tweets and Facebook posts does not determine
the existence of evidence of social impact in social media. For example, project 2 has 403 tweets
and 423 Facebooks posts, but it has no evidence of social impact on social media. In contrast,
project 9 has 62 tweets, 43 Facebook posts, and 2 pieces of evidence of social impact in social
media, as shown in Table 11.
The ratio of tweets/Fb posts to evidence is 0.43%, and it differs depending on the project, as
shown below in Table 12. There is one project (P7) with a ratio of 4.98%, which is a social
impact coverage ratio higher than that of the other projects. Next, a group of projects (P3, P9,
P10) has a social impact coverage ratio between 1.41% and 2,99%.The next slot has three proj-
ects (P1, P4, P5), with a ratio between 0.13% and 0.46%. Finally, there are three projects (P2,
P6, P8) without any tweets/Fb posts evidence of social impact.
Considering the three strategies for obtaining data, each is related differently to the evi-
dence of social impact. In terms of the social impact coverage ratio, as shown in Table 13, the
Table 10. Relation of tweets/Fb posts with evidence of social impact.
Total tweets/ Fb posts 5,350
Total tweets/ Fb posts with evidence 23
Social Impact Coverage Ratio 0,43%
https://doi.org/10.1371/journal.pone.0203117.t010
Social impact of research in social media
PLOS ONE | https://doi.org/10.1371/journal.pone.0203117 August 29, 2018 14 / 20
most successful strategy is number 3 (searchable research results), as it has a relation of
17.86%, which is much higher than the ratios for the other 2 strategies. The second strategy
(acronym search) is more effective than the first (profile accounts),with 1.77% for the former
as opposed to 0.27% for the latter.
Once tweets and Facebook posts providing linkages with information about social impact
(ESISM)were identified, we classified them in terms of quantitative (QUANESISM) or qualita-
tive evidence (QUALESISM)to determine which type of evidence was shared in social media.
Table 14 indicates the amount of quantitative and qualitative evidence identified for each
search strategy.
Discussion
First, the results obtained indicated that the SISM methodology aids in calculating the social
impact coverage ratio of the research projects selected and evaluating whether the social
impact of the corresponding research is shared by citizens in social media. The social impact
coverage ratio applied to the sample selected is low, but when we analyse the SICOR of each
project separately, we can observe that some projects have a higher social impact coverage
ratio than others. Complementary to altmetrics measuring the extent to which research results
reach out society, the SICOR considers the question whether this process includes evidence of
Table 11. Total number of tweets and Facebook posts with evidence of social impact.
Project Tweets Tweets with evidence of potential/real social impact Facebook posts Facebook posts with evidence of potential/real social impact
Project 1 965 2 585 0
Project 2 403 0 423 0
Project 3 903 0 396 1
Project 4 26 2 41 1
Project 5 418 1 16 0
Project 6 302 0 74 0
Project 7 157 6 64 5
Project 8 65 0 236 0
Project 9 62 1 43 1
Project 10 124 3 47 0
TOTAL 3,425 15 1,925 8
https://doi.org/10.1371/journal.pone.0203117.t011
Table 12. Social impact coverage ratio per project.
Projects Total tweets/ Fb
posts
Total tweets/ Fb posts with potential/real social
impact
Social Impact Coverage
Ratio
Project 1 1,550 2 0,13%
Project 2 826 0 0,00
Project 3 67 2 2,99%
Project 4 434 2 0,46%
Project 5 376 1 0,27%
Project 6 376 0 0,00
Project 7 221 11 4,98%
Project 8 301 0 0,00
Project 9 105 2 1,90%
Project
10
171 3 1,75%
https://doi.org/10.1371/journal.pone.0203117.t012
Social impact of research in social media
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potential or real social impact. In this sense, the overall methodology of SISM contributes to
advancement in the evaluation of the social impact of research by providing a more precise
approach to what we are evaluating.
This contribution complements current evaluation methodologies of social impact that
consider which improvements are shared by citizens in social media. Exploring the results in
more depth, it is relevant to highlight that of the ten projects selected, there is one research
project with a social impact coverage ratio higher than those of the others, which include proj-
ects without any tweets or Facebook posts with evidence of social impact. This project has a
higher ratio of evidence than the others because evidence of its social impact is shared more
than is that of other projects. This also means that the researchers produced evidence of social
impact and shared it during the project. Another relevant result is that the quantity of tweets
and Fb/posts collected did not determine the number of tweets and Fb/posts found with evi-
dence of social impact. Moreover, the analysis of the research projects selected showed that
there are projects with less social media interaction but with more tweets and Fb/posts con-
taining evidence of social media impact. Thus, the number of tweets and Fb/posts with evi-
dence of social impact is not determined by the number of publication messages collected; it is
determined by the type of messages published and shared, that is, whether they contain evi-
dence of social impact or not.
The second main finding is related to the effectiveness of the search strategies defined.
Related to the strategies carried out under this methodology, one of the results found is that
the most effective search strategy is the searchable research results, which reveals a higher per-
centage of evidence of social impact than the own account and acronym search strategies.
However, the use of these three search strategies is highly recommended because the combina-
tion of all of them makes it possible to identify more tweets and Facebook posts with evidence
of social impact.
Another result is related to the type of evidence of social impact found. There is both quan-
titative and qualitative evidence. Both types are useful for understanding the type of social
impact achieved by the corresponding research project. In this sense, quantitative evidence
allows us to understand the improvements obtained by the implementation of the research
results and capture their impact. In contrast, qualitative evidence allows us to deeply under-
stand how the resultant improvements obtained from the implementation of the research
Table 13. Social impact coverage ratio per search strategy.
Total tweets/ Fb
posts
Total tweets/ Fb posts with potential/
real social impact
Social Impact
Coverage Ratio
Strategy 1 (profile accounts) 5,096 14 0,27%
Strategy 2 (acronym search) 226 4 1,77%
Strategy 3 (searchable
research results)
28 5 17,86%
Total 5,350 23
https://doi.org/10.1371/journal.pone.0203117.t013
Table 14. Amount of each type of evidence for each search strategy.
Strategy 1 Strategy 2 Strategy 3
Profile twitter Facebook page Acronym Search (Twitter) Searchable Research Result
(Twitter)
ESISM-QUANESISM ESISM
QUALESISM
ESISM
QUANESISM
ESISM
QUALESISM
ESISM QUANESISM ESISM
QUALESISM
ESISM QUANESIM ESISM QUALESISM
3 3 6 2 1 3 4 1
https://doi.org/10.1371/journal.pone.0203117.t014
Social impact of research in social media
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results are evaluated by the end users by capturing their corresponding direct quotes. The
social impact includes the identification of both real and potential social impact.
Conclusions
After discussing the main results obtained, we conclude with the following points. Our study
indicates that there is incipient evidence of social impact, both potential and real, in social
media. This demonstrates that researchers from different fields, in the present case involved in
medical research, public health, animal welfare and genomics, are sharing the improvements
generated by their research and opening up new venues for citizens to interact with their
work. This would imply that scientists are promoting not only the dissemination of their
research results but also the evidence on how their results may lead to the improvement of
societies. Considering the increasing relevance and presence of the dissemination of research,
the results indicate that scientists still need to include in their dissemination and communica-
tion strategies the aim of sharing the social impact of their results. This implies the publication
of concrete qualitative or quantitative evidence of the social impact obtained. Because of the
inclusion of this strategy, citizens will pay more attention to the content published in social
media because they are interested in knowing how science can contribute to improving their
living conditions and in accessing crucial information. Sharing social impact in social media
facilitates access to citizens of different ages, genders, cultural backgrounds and education lev-
els. However, what is most relevant for our argument here is how citizens should also be able
to participate in the evaluation of the social impact of research, with social media a great source
to reinforce this democratization process. This contributes not only to greatly improving the
social impact assessment, as in addition to experts, policy makers and scientific publications,
citizens through social media contribute to making this assessment much more accurate.
Thus, citizens’ contribution to the dissemination of evidence of the social impact of research
yields access to more diverse sectors of society and information that might be unknown by the
research or political community. Two future steps are opened here. On the one hand, it is nec-
essary to further examine the profiles of users who interact with this evidence of social impact
considering the limitations of the privacy and availability of profile information. A second
future task is to advance in the articulation of the role played by citizens’ participation in social
impact assessment, as citizens can contribute to current worldwide efforts by shedding new
light on this process of social impact assessment and contributing to making science more rele-
vant and useful for the most urgent and poignant social needs.
Supporting information
S1 File. Interrater reliability (kappa) result. This file contains the SPSS file with the result of
the calculation of Cohen’s Kappa regards the interrater reliability. The word document
exported with the obtained result is also included.
(ZIP)
S2 File. Data collected and SICOR calculation. This excel contains four sheets, the first one
titled “data collected” contains the number of tweets and Facebook posts collected through the
three defined search strategies; the second sheet titled “sample” contains the sample classified
by project indicating the ID of the message or code assigned, the type of message (tweet or
Facebook post) and the codification done by researchers being 1 (is evidence of social impact,
either potential or real) and 0 (is not evidence of social impact); the third sheet titled “evidence
found” contains the number of type of evidences of social impact founded by project (ESISM--
QUANESIM or ESISM-QUALESIM), search strategy and type of message (tweet or Facebook
Social impact of research in social media
PLOS ONE | https://doi.org/10.1371/journal.pone.0203117 August 29, 2018 17 / 20
posts); and the last sheet titled “SICOR” contains the Social Impact Coverage Ratio calculation
by projects in one table and type of search strategy done in another one.
(XLSX)
Acknowledgments
The research leading to these results received funding from the 7
th
Framework Programme of
the European Commission under Grant Agreement n˚ 613202. The extraction of available
data using the list of searchable keywords on Twitter and Facebook followed the ethical guide-
lines for social media research supported by the Economic and Social Research Council (UK)
[36] and the University of Aberdeen [37]. Furthermore, the research results have already been
published and made public, and hence, there are no ethical issues.
Author Contributions
Conceptualization: Teresa Sorde
´-Martı
´, Ramon Flecha.
Investigation: Cristina M. Pulido, Gisela Redondo-Sama, Teresa Sorde
´-Martı
´, Ramon Flecha.
Methodology: Teresa Sorde
´-Martı
´, Ramon Flecha.
Supervision: Teresa Sorde
´-Martı
´, Ramon Flecha.
Writing – original draft: Cristina M. Pulido, Gisela Redondo-Sama.
Writing – review & editing: Teresa Sorde
´-Martı
´, Ramon Flecha.
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Social impact of research in social media
PLOS ONE | https://doi.org/10.1371/journal.pone.0203117 August 29, 2018 20 / 20
... This research is an exploratory study that integrated two different research techniques developed within the Communicative Methodology [57]. Firstly, the Social Impact in Social Media Methodology (SISM) [58] was used to analyse comments on the video game posted on the social network Twitter. At the same time, six communicative interviews were conducted with users, one of whom was a family member of a user. ...
... This research is an exploratory study that integrated two different research niques developed within the Communicative Methodology [57]. Firstly, the Social Im in Social Media Methodology (SISM) [58] was used to analyse comments on the v game posted on the social network Twitter. At the same time, six communicative views were conducted with users, one of whom was a family member of a user. ...
... The Social Impact in Social Media Methodology (SISM) [58] was used for the qu tative and qualitative content analysis of the selected Twitter sample [56]. The follo steps were followed for the selection and extraction of the data: ...
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The growth and impact of video games in education at an international level is a reality. Research shows that gamers can increase their knowledge, skills, and behavioural flexibility. However, there has been no in-depth research into the relationship between current video games and the key competences for lifelong learning set out by the European Commission. This research focuses on learning acquisition through playing the popular game “Animal Crossing: New Horizons”. The Communicative Methodology has been used in this research through, on the one hand, use of the Social Impact in Social Media (SISM) method involving the analysis of 1000 comments posted on the social network Twitter and, on the other hand, through communicative inter, sanviews with five gamers and a family member of a user. The results show that the Animal Crossing video game promotes learning achievements regarding literacy, multilingualism, mathematical skills, digital competence, social skills, citizenship, entrepreneurship, and cultural awareness.
... Another action plan that can be realized is to use social media to reach large audiences, which could effect a reduction in vaccine hesitancy among individuals. Social media is emerging as one of the most functional tools used to inform the population today [94,95]. A social media collaboration between government agencies and volunteers could create a snowball effect that will inform the large masses about vaccine hesitancy. ...
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Starting in early 2020, the COVID-19 pandemic has been responsible, worldwide, for millions of deaths and patients with long-COVID syndrome. In an attempt to stop the spread of the virus, the blanket administration of COVID-19 vaccines proved to be the most effective measure, yet the existence and availability of functional vaccines did not and, still, do not ensure the willingness and intent of people to be vaccinated. This study assessed the similarities and differences in vaccine fears and vaccine hesitancy through between clusters of subjects: people that were not infected with COVID-19, people that had COVID but did not develop long-lasting symptoms, and people that were infected with COVID and developed long-COVID syndrome. From the sample of 1111 Italian people, it was found that individuals who experienced mild symptoms showed higher vaccine hesitancy (confidence, complacency, and collective responsibility) than those who did not contract COVID-19. People affected by long-COVID showed a lower overall hesitancy than individuals who had COVID-19 without incurring long-lasting symptoms and, thus, essentially resembled people who had no experience of COVID-19 infection in terms of the vaccine hesitancy scores. Vaccine fear remained unchanged across all three of the examined clusters.
... O impacto social está associado à geração de conhecimento e realização de atividades capazes de trazer benefícios para a sociedade (WOOD JR. et al., 2016). A pesquisa impacta em diferentes domínios, como por exemplo, no risco na agricultura (quando evidencia pesquisas de IC nessa área e seus impactos quanto a ineficiência de processos) e no risco para a saúde (por extensão, aprendizado de práticas de atendimento humanizado) (PULIDO et al., 2018). Godin e Dore (2005) Por conseguinte, pode-se depreender que a pesquisa de IC tende a ser pesquisada como objeto de análise para prover conhecimento para ingresso no mundo acadêmico. ...
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... These online platforms offer ways to build communities of special interest groups and connect individuals to people and places -especially in times of restrictions on movement and socialising. Social media research also offers a way to explore trends and sentiments in society, which is, for example, used by academic researchers (Bertrand et al. 2013;Pulido et al. 2018) and governmental organisations (Social Media Research Group 2016). ...
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... Specifically, many of the electronic rumours spread through mobile messaging applications is very difficult to catch at the initial stages unless it is notified by the users, and these short posts exists for short life span at the server. Similarly, microblogs communicated or shared via various interchangeable social media platform to other social mediums (i.e., WhatsApp to Facebook, Google+ to Instagram, Instagram to WhatsApp, youtube to WhatsApp, Facebook to WhatsApp and vice versa) differs in their messaging architecture and privacy restrictions of storing and retrieving policies that makes it difficult to identify the rumour words when they are encountered in microblogs [6]. Radio agencies and News channels also plays a vital role in sending of rumours through audio, video or conference communication, which becomes impossible to analyze and stop their transmissions at run-time, such contents once viewed in mobile phones are automatically auto-saved in the memory and hence, are transmitted to others at later point of time. ...
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... Social media is one of the pillars and means of disseminating information and the way we communicate. Therefore, systematic social media management is needed, and a guide and application are needed to detect in detail what things must be done on social media (Wooley, 2013;Pulido et al., 2018). Difficulties such as reading the intentions and thought patterns of others and the emotions they express are problems that must be resolved. ...
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Recently, the need to contribute to the evaluation of the scientific, social, and political impact of Social Sciences and Humanities (SSH) research has become a demand of policy makers and society. The international scientific community has made significant advances that have transformed the impact of evaluation landscape. This article reviews the existing scientific knowledge on evaluation tools and techniques that are applied to assess the scientific impact of SSH research; the changing structure of social and political impacts of SSH research is investigated based on an overarching research question: To what extent do scholars attempt to apply methods, instruments, and approaches that take into account the distinctive features of SSH? The review also includes examples of European Union (EU) projects that demonstrate these impacts. This article culminates in a discussion of the development of the assessment of different impacts and identifies limitations, and areas and topics to explore in the future.
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By combining scientific excellence with social involvement, M. S. Swaminathan has put himself in the tradition of the great agricultural researchers such as Von Liebich, Vavilov, De Vries, Haber and his friend and colleague Norman Borlaug that have defeated the Spectre of Malthus. His ability to use his knowledge and insights to find solutions for complex social problems made him one of the founding fathers of the Green Revolution. And one of the first that saw the drawbacks of the extensive use of water, fertilizer and pesticides that came along with it. He became a staunch advocate for the Evergreen Revolution towards an eco-friendly, resource-poor, sustainable agriculture that is based on science and technology and aims for nutrition security for all. Challenged with the perspective of feeding 9 or 10 billion people with sufficient and nutritious food and producing enough raw materials for the developing bio-based economy we have to keep on learning by doing research and combining its results with the experience of farmers and others. Yet there seems to be a lack of belief - at least in Europe - in human learning; a general distrust in science, which might lead to paralysis in agricultural development. Hence the biggest challenge is to bridge the gap between the sciences and society and to engage society in the development of science to meet the challenges of tomorrow.