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Text–Image Relationships in Tweets: Shaping the Meanings of an Epidemic

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Abstract

1. Background: While many studies analyze the functions that images can fulfill during humanitarian crises or catastrophes, an understanding of how meaning is constructed in text–image relationships is lacking. This article explores how discourses are produced using different types of text–image interactions. It presents a case study focusing on a humanitarian crisis, more specifically the sexual transmission of Ebola. 2. Methods: Data were processed both quantitatively and qualitatively through a keyword-based selection. Tweets containing an image were retrieved from a database of 210,600 tweets containing the words “Ebola” and “semen”, in English and in French, over the course of 12 months. When this first selection was crossed with the imperative of focusing on a specific thematic (the sexual transmission of Ebola) and avoiding off-topic text–image relationships, it led to reducing the corpus to 182 tweets. 3. Results: The article proposes a four-category classification of text–image relationships. Theoretically, it provides original insights into how discourses are built in social media; it also highlights the semiotic significance of images in expressing an opinion or an emotion. 4. Conclusion: The results suggest that the process of signification needs to be rethought: Content enhancement and dialogism through images have a bearing on Twitter’s use as a public sphere, such as credibilization of discourses or politicization of events. This opens the way to a new, more comprehensive approach to the rhetorics of users on Twitter.
Societies 2019, 9, 12; doi:10.3390/soc9010012 www.mdpi.com/journal/societies
Article
Text–Image Relationships in Tweets: Shaping the
Meanings of an Epidemic
Celine Morin 1,*, Arnaud Mercier 2 and Laetitia Atlani-Duault 3,4,5
1 Department of Communication, HAR, Paris Nanterre University, 92000 Paris, France
2 Media Studies Department, Institut Français de Presse, Paris Assas University, 75000 Paris, France;
arnaud.mercier@u-paris2.fr
3 IRD/CEPED, Sorbonne Paris Cité University, Paris V René Descartes, 75006 Paris, France;
laetitia.atlani-duault@ird.fr
4 Collège d’Études Mondiales (CEM), Fondation Maison des sciences de l’homme (FMSH),
75006 Paris, France
5 Mailman School of Public Health, Columbia University, New York, NY 10032, USA
* Correspondence author: Celine.morin@parisnanterre.fr
Received: 13 December 2018; Accepted: 24 January 2019; Published: 29 January 2019
Abstract: 1. Background: While many studies analyze the functions that images can fulfill during
humanitarian crises or catastrophes, an understanding of how meaning is constructed in
text–image relationships is lacking. This article explores how discourses are produced using
different types of text–image interactions. It presents a case study focusing on a humanitarian
crisis, more specifically the sexual transmission of Ebola. 2. Methods: Data were processed both
quantitatively and qualitatively through a keyword-based selection. Tweets containing an image
were retrieved from a database of 210,600 tweets containing the words “Ebola” and “semen”, in
English and in French, over the course of 12 months. When this first selection was crossed with the
imperative of focusing on a specific thematic (the sexual transmission of Ebola) and avoiding
off-topic text–image relationships, it led to reducing the corpus to 182 tweets. 3. Results: The article
proposes a four-category classification of text–image relationships. Theoretically, it provides
original insights into how discourses are built in social media; it also highlights the semiotic
significance of images in expressing an opinion or an emotion. 4. Conclusion: The results suggest
that the process of signification needs to be rethought: Content enhancement and dialogism
through images have a bearing on Twitter’s use as a public sphere, such as credibilization of
discourses or politicization of events. This opens the way to a new, more comprehensive approach
to the rhetorics of users on Twitter.
Keywords: social media; Twitter; text–image relationships; pragmatics; health crisis
1. Introduction
Images are a powerful vector of signification that social media users resort to more and more.
Although it seems evident that understanding text–image relationships is of great importance to
grasping the full meaning of any message, it is unfortunately too common to read studies about
Twitter in which tweets are treated exclusively as text data or, more rarely, as platforms for sharing
visuals. Many of these studies focus on social functions involving images such as the rise of ‘citizen
journalists’ [1], the documentation and management of live catastrophes [2] and political crisis [3], or
they limit themselves to analyzing how images contribute to the discursive construction of an event
[4,5].
The vast majority of studies on Twitter focus on text messages. The issues studied are rich and
varied, exploring, for example, the syntax and writing styles produced under Twitter’s 140-character
Societies 2019, 9, 12 2 of 18
constraint or focusing on Twitter as an election propaganda resource, examining the
self-presentation of candidates, the ideological content, or the flashes of wit that this format allows
politicians to exhibit in writing. Others deal with texts as unveilings of personal privacy. The fact
that little is said about the accompanying images is largely due to real technical difficulties. The
standard software for the extraction of tweets often takes little account of images and videos, which
do not generate automatic classification. For a corpus of thousands of tweets (to say nothing of a
corpus of millions), computer processing of texts is already complicated, and adding images in their
various formats and functions can quickly become difficult and time-consuming. Yet images are
becoming increasingly important in social media, as can be seen in the success of networks featuring
images such as Instagram, Pinterest or Snapchat, and of video networks like Facebook Live or
Periscope. The strategies developed by users to make their discourses more visible are often based
on images, which makes, in the words of Barthes [6], the ‘relay’ relationship (when texts add
information to images) more frequent than the ‘anchorage’ relationships (when texts fix the
meaning), therefore encouraging researchers to explore its variations on social media. Many articles
base their visual corpus on extractions of images coming from social networks. They expose
methodological difficulties [7,8] or search traces of a new visual culture, especially through selfies
[9,10], the art of exposing subjectivity [11] or self-esteem enhancement [12].
The relationship between text and image is admittedly a very specific topic of research but is
essential to understand meanings on social media. Detaching texts from images or postulating the
superiority of texts over images runs the risk of skewing research results. This paper provides
theoretical and methodological tools and suggestions for working in this as-yet largely unexplored
field and emphasizes the necessity of rigorous qualitative selection and analysis of tweets,
proceeding first by subsamplings and successive filtering, and then by semiodiscursive analysis.
This paper offers renewed methodological insights into manual selection and coding of tweets. With
this objective, we chose to illustrate our approach through a case study focusing on health during a
humanitarian crisis, the 2014 Ebola epidemic regarding its least-understood path of transmission:
Sexual transmission from survivors. We compared our results with an earlier analysis of Twitter
texts in order to understand if and how images corroborate or inflect texts.
The 2014 Ebola outbreak and the sexual transmission makes it a fitting case study to understand
tweets in a new way, as it raises worrying questions concerning potential new outbreaks, the
emergence of a ‘new AIDS’, and the contamination of territories that were previously spared. Ebola
represents the first pandemic in the era of widespread use of social media. The sensitivity of this
epidemic has resulted in extreme speech and crystallizes specific problems, such as: The expression
and sometimes confrontation or confusion of different registers of discourse (information,
disinformation, prevention, and rumor); the undermining of public health policies; or questions
about which prevention discourses are rhetorically conducive to text–image combinations (posters,
graphics, etc.).
Analysis of Twitter images is encouraged by the rich visual potential of Ebola, including
imagery about Africa at the time that the virus appeared as a global threat [13]; its visually dreadful
symptoms; or the sexual particularity of the transmission. As Sander L. Gilman [14] underlined,
‘icons of disease appear to have an existence independent of the reality of any given disease.’ If ‘the
creation of the image of [the disease] must be understood as part of this ongoing attempt to isolate
and control disease’, the sharing of Ebola images on Twitter should be a fruitful field for studying
how people receive and rank official information and rumors, for understanding prevention
addresses and warning signs, and for investigating discourses of reassurance and fear during a
global humanitarian crisis.
We propose a four-category classification of the relationships between texts and
images—illustration, commentary, repetition, and complementarity. Discourses appear particularly
amplified when users instrumentalize text or image to go beyond the mere dissemination of
information and to express emotion, political positions, doubts or personal stances. Our results show
how the analysis of text–image combinations reveals meanings that would not have been discerned
otherwise, offering opportunities for renewed theoretical and empirical perspectives on text–image
Societies 2019, 9, 12 3 of 18
combinations. The supposedly restrictive 140-character limit of Twitter reveals in practice an
inventive plasticity: Texts and images are not constrained to the designated spaces, and discourses
take advantage of this. Furthermore, our conjoint analysis shows that in cases where texts are quite
standardized, images can be used to inflect the event’s framing; equally, a single image can be
shared with contradictory discourses, thus conveying social debates. Often, even official poster
preventions end up being moot points rather than being accepted as information.
1.1. Text–Image Relationships in Tweets
While many studies take into account the functions that images can fulfil during humanitarian
crises or catastrophes, an understanding of how meaning is constructed in text–image relationships
is lacking. Despite the part played by imagination and real images about Ebola, and despite the fact
that social media have been scientifically studied to understand social discourses about Ebola, there
has been no research to date on the construction of discursive strategies in a holistic way. In a time of
uncertainty such as that triggered by an Ebola epidemic, how do people use social media to
disseminate information, to participate in debates or controversies, to be heard and to express
emotions?
Text–image combinations have been the subject of numerous articles that focus on the functions
carried out by images. Twitter can incite a ‘seamless convergence of photographic and textual
information from everyday ‘citizen journalists’ that can extremely rapidly disseminate a picture [1].
Images of catastrophes such as the storm that hit Belgium’s Pukkelpop music festival in August 2011
offer rapid and undeniable proof of the damages [2]. When analyzing the 2011 UK riots, Vis et al. [3]
emphasized the ‘complex relationships between actual and virtual space involved with the
production and distribution of Twitter images’: By uploading pictures of a crisis, users participate in
an eye-witnessing process. The researchers further described the sharing of television screenshots as
a new relationship between audiences and the media content that people turn into a ‘still, shareable
image’. While conducting a study of images of Ebola on Instagram and Flickr, Seltzer et al. [5]
identified four themes of images (health care workers; West Africa; Ebola virus; and artistic
representations of Ebola) and four themes mixing images and embedded text (facts, fears, politics,
and jokes), while 22% of the collected images were irrelevant. Their results showed that social media
were used for information dissemination or information exchange and that there were differences
between platforms: Instagram images were mostly jokes or simply irrelevant, while Flickr images
primarily represented health care professionals at work. Seltzer et al. therefore mentioned the need
for a better use of the particular type of communication offered by social media for health messaging
and education. Among the images in their sample that contained embedded text, 23% were jokes
that they categorized into five main themes: Political jokes, mocking of fearful people, racial
stereotypes, costumes (e.g., for Halloween), and the implication that someone or something has
Ebola. They noted no necessary equivalence between the content of the joke and the reality of
transmission modes. They therefore supported what might be called an ‘upward’ communication
strategy, adopting the visual codes of Twitter users to counter misinformation. They emphasized
that ‘social media kinetics remind us of classic lessons in communication, which emphasize not just
content, but also source, medium, and tone.’ This call for engaging with users on their own terms is
echoed by Kapp, Hensel, and Schnoring [15] who, drawing on uses and gratifications theories, asked
how people screen information according to their motivations (‘diversion, personal relationships,
personal identity and surveillance’). Other studies underlined the need to understand emotions at
work in tweets containing images. When studying Twitter images of the 2011 Egyptian revolution,
Kharroub and Bas [4] showed how images contained more ‘efficacy-eliciting’ content than
‘emotionally arousing images,’ whereas other studies such as the one led by Papacharissi and de
Fatima Oliveira [16] found that Twitter texts contained signs of emotions and personal opinions. The
preceding studies have largely categorized images according to their discursive register or rhetorical
role in a sensitive situation (storm, insurrection, health crisis, etc.). Our study proposes to analyze
various strategies of articulation between text and image that emerged during a specific
humanitarian crisis.
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1.2. Ebola and Social Media
The relationship between Ebola and social media has been the subject of numerous scientific
papers, which agree on social media’s potential for information dissemination [17–20].
Rodriguez-Morales, Castaneda-Hernandez, and McGregor [21] note a 21-fold ‘spike’ in tweets in the
24 h following the announcement of the first Ebola diagnosis on US soil on 30 September 2014 (see
also [22]). This data suggests that users echo news activity, which is encouraged by Twitter’s
nonhierarchical nature [23], and that they do so with relative autonomy. Odlum and Yoon showed
some pronounced spikes when official announcements concerned actual American cases of Ebola
(‘American doctor found Ebola positive’ and ‘American fighting Ebola coming home to the US’),
revealing a clear concern about outbreaks that are close geographically or culturally. However,
outbreaks do not need to be geographically close to trigger substantial and immediate responses
online: More tweets were posted from the USA than from the countries actually affected by the
epidemic [24].
These studies identify a number of activities in which the use of Twitter offers major
advantages: Information dissemination; prevention; outbreak mapping; facilitating communication
between scientists, policymakers, and citizens [5,19]; active participation from users; recognition of
popular discourses, including both their substantive content and their degree of concern. Regarding
the latter, Odlum and Yoon showed that popular communication includes both public concern and
echoing of news alerts. The question of media effects re-emerges here as media uses are suspected to
cause disproportionate fears, although this perspective tends to ignore reassuring coverage, as
Ungar [25] showed regarding the Ebola outbreak in Zaire. Seltzer et al. clearly described the paradox
regarding Ebola in the US: While as of December 2014 there had been only two deaths from Ebola in
the US (during the same time, West Africa counted over 5000 deaths), Americans ranked Ebola third
in their list of biggest health problems (just after costs and access to health care). As much as the
importance of information dissemination in social media is irrefutable, the insistence on the sole
texts of the tweets appears as a bias that makes other uses less visible. As we will argue, analyzing
texts and images together offers the opportunity to refine the analysis of the different kinds of
information and the different ways of spreading them.
2. Materials and Methods
Data were processed both quantitatively and qualitatively through a keyword-based selection,
which, as studies have shown [17,20,22], is the most efficient methodology to narrow down a
massive corpus. Using “Ebola” and “semen” as keywords, in English and in French, we retrieved a
total of 210,600 tweets (including retweets) over the course of 12 months (1 April 2014 to 1 April
2015). During this first selection, data anonymization was carried out in accordance with ethical
standards. While information was available regarding the time distribution, exploration of these
data was largely irrelevant to our study. The construction of the corpus consisted of a succession of
qualitative analysis: We took two core samples of 6000 tweets that were manually coded to select
two lists of keywords, one that would indicate the presence of an image and one that would indicate
the discussion around the sexual transmission of Ebola. We retrieved 14,316 tweets containing an
image. It should be noted that we encountered several tweets articulating similar texts and similar
images (particularly in the case of tweets disseminating information). Because there was no scientific
value in keeping such duplicates, we chose to remove them, producing a corpus of 12,900 tweets.
Still, a large number of these tweets did not have any link to the sexual transmission of Ebola,
leading us to construct another core sample to identify a relevant keywords list. The crossing of this
two-keywords-based selection led to a corpus of 560 tweets, from which manual coding deleted all
tweets nonrelated to the sexual transmission of Ebola. The final corpus consisted of 182 tweets.
Societies 2019, 9, 12 5 of 18
3. Results
3.1. The Need for Qualitative Methods: Subsampling, Successive Sievings, and Semiodiscursive Analysis
Users combine several levels of discourse when they produce articulations of text and image
online, making it impossible to decipher the various meanings by simply analyzing text and/or
images separately. Rather, articulating the two produces a cascade of meaning effects that can be
best seized by a semiodiscursive analysis, drawing attention to what is given access to
representation and what is not; and with which framings. A qualitative analysis of text–image
relationships therefore benefits from successive re-examinations of different samples of tweets
extracted from the sources, making it possible to identify original discursive forms that a
quantitative analysis would otherwise have concealed. Therefore, while a keyword-based selection
has been proven reliable, subsampling, successive filterings, and semiodiscursive analysis have
proven to be useful tools to identify, select, and interpret complex productions of meaning, notably
in text–image articulations. This is all the truer in that a qualitative analysis of tweets rapidly shows
how users opportunistically reference “trendy” subjects when discussing topics that actually have
little to do with them. Our research was rapidly confronted with individuals evoking Ebola and
semen for other reasons than actually discussing the topic at hand (i.e., the sexual transmission of
Ebola), or posting images of Ebola that had little to nothing to do with the disease or the health crisis.
This led us to further tap into primary sources in order to select tweets that were truly related to
text–image relationships during a health crisis. We emphasize that a purely quantitative selection of
a corpus in these sources would not have identified these tweets. For this reason, our qualitative
analysis of tweets relied first and foremost on constant subsampling, identifying both the generic
terms indicating the presence of an image and the words most used to discuss the sexual
transmission of Ebola. Our investigation, based on selecting 6000 tweets out of the corpus, reading
them, and manually coding them, confirms that the analysis of complex meanings presented as a
combination of text and image cannot skip a human interpretative stage, which itself requires
constant recalibrations.
The first step was to select all tweets containing an image. This was made easier by our software
(developed by the firm Semiocast), which automatically translated and copied the URL of the image
into the tweet. This allowed us to search for generic terms indicating the presence of an image in a
tweet. Taking into account the time that could be devoted to qualitative analysis with such a huge
corpus, an initial sampling target of 6000 tweets was selected that could be widened if the results did
not answer our initial interrogation or if they led to new hypotheses. The operative keywords were
obtained after a first sampling pass, using these 6000 tweets containing “http” and opening every
link: The final list contained the words “photo”, “instagram”, and “ifunny.” In this first pass, a total
Tweets containing
the words "Ebola"
and "semen" over
12 months
(April 1st 2014 to April
1st 2015)
Core
sample
to
identify
keywords
related
to
the
sexual
transmiss ion
of
Ebola
Core
sample
to
identify
keywords
indicating
the
presence
of
an
image
Collection
of
tweets
containing
an
image
Qualitative
coding:
manual
removal
of
strictly
identical
text-image
articulations
Crossing
of
keywords
indicating
an
image
and
related
to
the
sexual
transmission
Qualitative
coding:
manual removal
of
non
Ebola-related
tweets
Societies 2019, 9, 12 6 of 18
of 14,316 tweets containing an image were identified in the 210,600 tweets containing the words
“Ebola” and “semen”. Because users easily resort to Twitter’s image feature, and because this feature
was automatically translated in our database, the word “photo” was pervasive (916 tweets
containing “photo” out of the 6000 compared to only 11 and 9 tweets for “Instagram” and “ifunny”).
Simultaneously, the first selection of 210,600 tweets containing “Ebola” and “semen” was not precise
enough to narrow down tweets discussing the sexual transmission of Ebola. A first core sample of
6000 tweets allowed to identify the fourteen keywords that were selected: “abstinence”, “swallow-“,
“intercourse”, “condom”, “unprotected sex”, “hav-sex”, “fellatio”, “masturbate”, “secretion”,
“semen”, “sex”, “sperm”, “vagina”, “sleep”, and “STD”, and their French equivalents. This
qualitative selection resulted from an analysis of the discourses at work: Inside jokes, particular
vocabulary of the event, popular expression, online vernacular, acronyms, etc. The application of
this two-keywords-based selection led to a corpus of 560 tweets.
The preliminary qualitative analysis of these 560 tweets led us to return to the corpus to refine
it, because our first results showed that the collected images had still surprisingly little to do with
Ebola. The word “Ebola” was mostly used for other objects or contexts, with stylistic devices, some
humoristic (“This soap is $195, it better wash Ebola, wash HIV, wash Malaria, shit.... It better wash
all my sins away”, “Oh my god stop tryin to kiss me you have ebola”) or metaphorical (“milk give
you aids Ebola tetanus and crabs too”; a high school picture accompanied by the hashtags “#School
#Friends #Ebola #Aids”). The vast majority of tweets referenced the virus only as a way to attract
attention or to take part in a discussion, making most of the corpus off-topic and requiring the
elaboration of a new and more precise corpus. The pervasive use of hashtags caused a real diversion
of meaning. Rather than contributing to a conversation around a theme, some Twitter users—out of
pure opportunism and the desire to be more visible—inserted themselves into the conversational
thread of a hashtag even when not addressing the hashtag theme. (As a side note, researchers should
use great caution when considering the metrics of tweets behind the most popular hashtags. When a
hashtag is used, the probability is high that parasitic and opportunistic tweets distort metrics by
wrongly amplifying them). Therefore, to avoid off-topic text–image relationships, we coded
qualitatively the 560 tweets and removed all the ones that were strategically using Ebola as a joke or
a metaphor and we kept only the ones addressing the sexual transmission of Ebola. This produced a
final corpus of 182 tweets.
3.2. Four Text–Image Relationships During Ebola Epidemics
Our data indicated four kinds of text–image relationships: Illustration; commentary; repetition;
and complementarity. As Martinec and Salway [26] have shown, new media are prone to displaying
text–image relationships with equal status (when one does not modify the other or when each one
modifies the other). Our four-category classification shows how users employ or sometimes twist
the technical devices so that posted images and texts produce the intended meaning in their
reciprocal modification.
1. Illustration. A majority of the images (56%) are an illustration of the text or rather of the
thematic focus addressed in the tweet. Illustrations are interesting as they demonstrate how
variably the event can be framed. Many offer a spectacular image of the disease by showing
doctors wearing ultraprotective suits. The disease is more frequently portrayed using those
who help the patients than the patients themselves. Another visual is the molecular
representation of the virus, which is more or less stylized. Thanks to its specific form and
frequency of appearance, this image can trigger an immediate identification of the subject
matter, like doctors in full protection suits, but without the anxiety impression.
Societies 2019, 9, 12 7 of 18
(Illustration).
2. Commentary. Text–image combinations can also be used to provide commentary (21%), for
example when texts contradict or doubt the information contained in the image (“RT
@Th…:@na…: I believe it false to say semen can transmit Ebola Body fluids yes. Semen ain’t”
accompanied by a screenshot of the WHO’s FAQ webpage mentioning the sexual transmission
of Ebola).
(Commentary).
3. Repetition was also noted (16%) between text and image: In order to highlight the information,
users tweet the same text as the one contained in the image. The 140-character constraint
sometimes pushes users to sum up the information contained in the image: For example, when
Societies 2019, 9, 12 8 of 18
displaying a screenshot of the transmission section of the virus’s Wikipedia page, one tweet
summarizes “WAYS YOU CAN GET EBOLA: Sweat, Feces, Breast Milk, Tears, Vomit, Semen,
Urine etc.. The image area thus becomes word-based, while the text area contains hooks whose
purpose can be to both summarize the content and to encourage the reading of the whole text.
(Repetition).
4. Complementarity. Finally, we noted complementarity between text and image, as when a tweet
comments on a WHO prevention poster advising to “Abstain from sex if you start feeling ill.
#Ebola” by adding “FACT: CDC advises #Ebola virus can remain in semen for >3months after
recovering from Ebola. #Abstain or #safesex.” Images are often a second discourse, a way of
saying something more than what is in the text.
(Complementarity).
Half of the tweets in our sample disseminated information without any added comment and
contained an illustration as a visual. In one set comprising 102 tweets, news articles were shared
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using a common structure: The text contained the title of the publication while the image was the
same illustration as is displayed on the website page to which the tweet links. One can assume this is
due to the fact that newspaper websites make it possible to tweet their article with a predetermined
format, making it very likely that those tweets originated from the reading of the article. These data
suggest Twitter is a preferred sphere for information dissemination.
Although tweets featuring news articles are the most likely to contain illustrative images, these
images are generally so varied that similar headlines are often accompanied with very different
illustrations, framing the event differently. They include pictures related to love, affection, and
sexuality, ranging from representations of spermatozoa or condoms to images showing affection
between individuals or calling for solidarity (e.g., a drawing of hands forming a heart).
(Shared image showing affection between individuals).
Ebola appears as a risk from within social links which people must protect themselves from and
as a threat to social links that must be preserved. Images also represent the virus itself, mostly in
microscopic view, thus offering a scientific perspective on the outbreak. Other newspaper articles
illustrate the Ebola outbreak with quotidian images of people in the streets. Lastly, a set of
interestingly similar images represents people fighting Ebola: Doctors examining a body, military
personnel explaining a planned action, caregivers helping a sick man, etc. One striking feature of this
set is that the depicted people are outdoors, on the move, and often wearing Hazmat suits: They are
shown taking care of a sick person or dealing with a dead body, putting a mask on, organizing a
protected area. Possible interpretations of the high frequency of Hazmat suits are that they evoke the
violence of the virus or that they represent health crisis management.
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(High frequency of Hazmat suits).
Moreover, as Joffe and Haarfooff [27] pointed out, the high frequency of westerners wearing
Hazmat suits gives the impression of a disease that was “controllable by western science, [as] part of
a world of make-believe, of science fiction”. Another example of how images frame the event
differently is that while all headlines containing the word condom” are very similar, the
accompanying illustrations vary. Although the texts are repetitious—“#Ebola survivors told to use
condoms - virus can live in semen for 70+ days”, “Ebola survivors told to use condoms to prevent
virus spreading”, “Male #Ebola survivors told to use condoms”, “Ebola survivors told2 use
condoms #West #SouthAfrica”—the images are as varied as condoms, a man washing his hands, a
man walking outdoors in front of a prevention sign bearing the slogan “Ebola is real”, or a
microscopic view of the Ebola virus.
(Man walking outdoors in front of a prevention sign bearing the slogan “Ebola is real”).
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Images successively represent Ebola as: An intimate and individual problem requiring safe-sex
measures; an interpersonal risk necessitating sanitary precautions; a health crisis that takes place in
the public sphere and which requires prevention campaigns; or a virus seen from a scientific
perspective.
A second set of 80 tweets shows more hybrid combinations of information dissemination and
personal comments. Here, text–image relationships are not homogeneously aimed at illustration
(only 13%) but vary between commentary (42%), repetition (28%), and complementarity (15%). As in
the previous set, messages consist of tweets or retweets of newspaper publications, but with the
addition of personal thoughts (“A bumpy road to Zero! First #Ebola case in wks in #Liberia. Sexual
transmission suspected. [link]”), or of completely original and personal messages (“Ebola travels
through a man’s sperm for 60 days after he’s been “Cured” and now both patient have been released
into the public. This is what I’ve been concerned about.”, “I don’t know how true this is but this
wikipedia page says Ebola can be spread via semen by men who survive it”). Information
dissemination becomes an opportunity to express one’s opinion.
Visuals have a significant importance in lending credibility to discourses. Often, images are
taken from official sources (poster, newspaper article, flyer, excerpt from a website, etc.), from public
health agencies or from online encyclopedias and accompany a text that is not related to the content
of the image. These images are shared by users who wish to engage with the debate on modes of
transmission, sometimes with an ideological perspective (“How come no one has announced to the
public that male semen can pass on the #Ebola 6 weeks–6 months after recovery”). A polemical use
of Twitter appears in a graphic montage where “Ebola” is written with the letterings of the 2008
Barack Obama campaign, the “O” making a strong link between Obama and Ebola. The author of
the tweet indicates his or her support for the Tea Party in the accompanying hashtags, therefore
seeking to politicize the management of the disease.
(“Ebola” written with the letterings of the 2008 Barack Obama campaign).
These tweets focus on fear, prevention, and reassurance, and with Ebola’s modes of
transmission. While tweets from the first set display unilateral rhetoric, tweets from the second set
are more dialogical, and reappropriations of images are widespread, as can be seen in the semantic
variations of a specific image. The CDC prevention poster “Facts about Ebola”, which states that
Ebola is not transmitted through air, water or food and that “Ebola poses no significant risk in the
United States”, is used to convey several different meanings. Some tweet authors laugh at
transmission via semen, commenting that “Ebola can be transmitted through semen lol” or “Or
having sex with someone who has/had Ebola because it can live in sperm for up to 82 days [followed
by two tongue-out emojis]”.
Societies 2019, 9, 12 12 of 18
(Laughs at transmission via semen).
Others share this prevention poster to reassure people: “to everybody who is freaking out about
Ebola”, “on the real for everybody who is freaking out about Ebola”, “for all you folks worrying
about #ebola, read this. Stay away from sweat, poop, blood, semen & pee of others.” A similar CDC
prevention poster explaining how the Ebola virus is transmitted (“bodily fluids”, “objects
contaminated” and “infected animals”) triggers this commentary: “Here is how you can get Ebola.
Sounds similar to AIDS…. Hmmmmm….#conspiracyhat #on.” Images whose original purpose is
preventive end up supporting other meanings, such as humoristic content or conspiracy theories.
3.3. Fear and Reassurance in Tweets: The Crucial Role of Images for Dramatizing and De-Dramatizing
A large proportion of tweets (38.8%) are related to fear, whether they express various fears or
serve to scare others, or they relativize the danger and offer reassurance regarding the low risk of
contracting Ebola. When fear arises in discourses on the sexual transmission of Ebola, tweets follow
the same pattern. Texts mostly share the information (“Research shows Ebola can be found in
survivors’ semen for weeks or months after recovery”, “90 days: Baeless [sic] period after a man
recovers from Ebola”), sometimes adding emphasis on the concern such news evokes (“It’s possible
that Ebola can be sexually transmitted. More on the scary news here.”) or eventually taking a
political stance on closing borders (“STD Alert: EBOLA in SEMEN 3 MONTHS! #SafeSex!
#HaltFlights! #SecureBorder!”). In juxtaposition with such texts, images firstly show symptoms: The
sharing of frightening images, particularly of blisters, evidently aims to illustrate how aggressive the
virus is. Secondly, screenshots from online encyclopedias are used to support what is hard and
maybe difficult-to-believe news (“Yu can get ebola thru unprotected sex from what im reading”,
“Ebola can live in a male survivor’s semen for two months!” accompanied by a Mask Face emoji).
Societies 2019, 9, 12 13 of 18
(User commenting on online encyclopedia).
Thirdly, microscopic images of the virus are common, often complemented by texts that are
themselves stylistically full of imagery. Many tweets personify Ebola, for example describing it as
“AIDS on steroids” or confer on it intentional and almost strategic actions (“Ebola thrives on human
kindness”), making it an active subject of the pandemic. A photomontage initially published by
American magazine Mother Jones but retweeted since without the corresponding article shows
spermatozoa heading towards the virus as if they were about to fertilize it.
(Personification of Ebola).
While fears are represented in images shared on Twitter, reassurance and relativization
discourses emerge through reappropriations of official posters. Users in our sample mostly shared
CDC’s prevention posters or a semihumoristic infographic published by the American news website
VOX, asking: Have you touched the vomit, blood, sweat, saliva, urine, or feces of someone who
might have Ebola?”, offering “no” for an only answer and ending with a terse “You do not have
Ebola.” Tweets in turn emphasized the fact that “you do NOT have #Ebola” enjoined people to calm
down or insisted on the low contagiousness of Ebola (“Worried about your chances of getting Ebola?
Societies 2019, 9, 12 14 of 18
Here’s a concrete guide on how it spread”), sometimes by putting symptoms into perspective
(“Feeling flu-ish? You don’t have Ebola.”)
(Tweet emphasizing the low contagiousness of Ebola).
At the same time, information texts within those reassuring tweets are contradictory, with some
users advising people to stop fearing Ebola and to “just use a condom”, while others stated that
“there is no evidence #Ebola remains in semen #FactsNotFear.” Users strategically backed their
recommendation with images borrowed from legitimate sources, such as the CDC. In fact, not only
did tweets calling for calm use prevention posters to legitimate their discourses but, more generally,
prevention posters were only shared to support those discourses. However, reassurance is not
always expressed in an attentive and comprehensive mode but rather in a biting and sometimes
quite aggressive manner. Directly addressing other people’s fear (“for all you folks worrying”,
“worried about your chances of getting Ebola?”, “don’t panic about Ebola”, “On the real for
everybody who is freaking out about Ebola”), tweets emphasized the actual modes of transmission
to call for “facts, not fear” as one hashtag puts it, to ask people to stop panicking about Ebola (“Don’t
panic about Ebola, know the facts”) or to mock the fear of Ebola (“Have you be reveling in someone
else’s feces, vomit or semen lately? Do they have #Ebola? If NO and NO, chill.”). Discrepancies
between what are considered as “real” risks and perceived risks are treated with irony.
Societies 2019, 9, 12 15 of 18
(Example of a reassuring tweet).
Images appear to be a major vector for discourses of fear and reassurance. A division can be
identified between the kinds of images that are shared depending on these ends. Fear is expressed
via visuals that present themselves as “real” (medical staff fighting the epidemic, corpses, sick
people, or symptoms), while reassurance discourses use official posters, infographics or screenshots
of online encyclopedias.
(Medical staff fighting the epidemic.) (User tweeting official statistics.)
These are hybrid images containing texts, and users emphasize they are presenting “facts”. The
official character of public health institutions is a main asset, particularly for users engaged in this
kind of dialogue against people they characterize as too easily afraid.
These results would not have been found if it were not for a simultaneous analysis of text and
image. During the early development of this study, the analysis of only the text in tweets showed
that Twitter was not just a place of contestation, information or interrogation, but also a place where
expressions of concern were frequent. With the additional analysis of images, however, those results
were more nuanced, since it revealed more clearly the presence of fear in tweets. Images such as
Hazmat suits, military personnel in action, quarantines or dead bodies construct a semiotics marked
strongly by anxiety. Confirming that signs of fear tend to be contained in images rather than text,
little repetition was found between text and image in those tweets.
4. Discussion
The study of the articulation “image–text” as used by Mitchell [28] is crucial to a more holistic
approach to Twitter itself but calls for specific developments. The objective here is to understand the
Societies 2019, 9, 12 16 of 18
argumentative or illustrative functions that Twitter users give them. The four kinds of interaction
identified by this paper (illustration, commentary, repetition, and complementarity) show how
varied and complex text–image relationships are, and how greatly this variety and complexity
influence the production of meaning online. While the illustration category was the most likely to
aggregate relatively homogeneous texts and images, further analysis revealed significantly different
framings of the subject, sometimes even contradictory ones, e.g., the insistence on the preservation of
the social links and the necessity of Hazmat suits, quarantines, and containment. Furthermore, our
analysis of text–image interaction shows that users deliberately take advantage of online
information dissemination to express their points of view by strategically using visuals to lend
credibility to their discourses. Twitter is not only a major sphere for information dissemination or for
self-expression nowadays: These two functions appear to be interrelated.
If our analysis had covered only text or only images, the results would have been different. A
quantitative selection of tweets based only on keywords would have missed important discursive
practices online: Only a mix of quantitative and qualitative analysis could identify these, based on
manual selection, coding, and interpretation and on successive comparisons between the sources
and the corpus. Our findings highlight the interdependence of the text and images online and the
semiotic significance of images in supporting a point of view or expressing an emotion and show
that for both the traditional media and Twitter users, images play a major part in the expression of
fear or reassurance. Official posters are instrumentalized for other purposes than information
dissemination. More broadly, our analysis provides original insights into how discourses are built in
social media via text–image combinations.
Methodologically speaking, it is limiting to consider text and image separately, firstly because
images are not only iconic representations: More than 55% of images do not contain any text, while
19% contain only text without any iconic representation, the remainder being a mix of iconic
representation with text. Moreover, our analysis of text and image together shows how dependent
meaning is on this relationship: Visuals express discourses of fear or insistence on affection that
could not be expressed this strongly in the text area, particularly in the case of the sharing of
newspaper articles whose titles are more neutral. In these cases, images are useful for expressing
emotive positions. At the same time, visuals themselves are polysemous, calling for a pragmatics of
text–image combinations: The strategic reappropriation of official images for making personal
discourses more credible is a development that surely evades the control of the authorities that
designed these visuals.
This study has certain limitations. No demographic or location information concerning the
users could be gathered. The huge number of humoristic images contained in the original data
collection biased the corpus so much that we were forced to remove them. A methodology
specifically adapted to take humoristic texts and images into account could result in an exploitable
corpus that would surely give valuable insight into the spectrum of meanings produced on Twitter.
Similarly, further research would benefit from a comparative analysis of different social media
platforms highlighting various types of discourses and rhetorics depending on the offered features.
Beyond those limitations, this case study on health in times of humanitarian crisis opens the
way for new perspectives of research, methodological as well as theoretical, regarding social media.
This new attention to text–image relationships contributes to better understanding of discursive
strategies on Twitter: It shows how users reappropriate a restrictive platform by twisting the uses of
images, allowing themselves to emphasize the text, to override the constraint of number of
characters, or to format the text in a way that the text box alone would never allow them to do. Users
do not mechanically accept the devices the platform offers. What is at stake is a new, comprehensive
approach to the rhetorics of users that is specific to Twitter: Its non-optional asynchronous dialogue
encourages users to make their messages noticeable and striking. To this end, text and images as
well as text–image relationships are not distinct components of online messages but are strategically
employed by users as simultaneous elements of the discourse they are trying to elaborate.
This, in return, means that the process of signification using social media needs to be rethought:
Content enhancement and dialogism through images have a bearing on Twitter’s use as a public
Societies 2019, 9, 12 17 of 18
sphere such as credibilization of discourses or politicization of events. Taking text and image into
account together promises to shed new light on Twitter’s devices, contents, and uses by allowing
them to be better understood not only by themselves, but first and foremost in their articulation.
Author Contributions: Conceptualization was led by all three authors on an original idea of A.M. C.M.
developed the methodology, conducted the analysis, and wrote most of the first draft of the article. A.M. helped
conduct the methodology and analysis and wrote consequent portions of the first draft. L.A.-D., who obtained
funding acquisition for this research, considerably helped review and edit the paper.
Funding: This research was funded by INSERM / IMMI (Institut National de la Sante et de la Recherche
Medicale, the French National Institute of Health and Medical Research / and Institut de Microbiologie et
Maladies Infectieuses, the Institute of Microbiology and Infectious Diseases).
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Murthy, D. Twitter: Microphone for the masses? Media Cult. Soc. 2011, 33, 779–789,
doi:10.1177/0163443711404744.
2. Terpstra, T.; Stronkman, R.; de Vries, A.; Paradies, G.L. Towards a realtime Twitter analysis during
crises for operational crisis management. In Proceedings of the 9th International ISCRAM Conference,
Vancouver, BC, Canada, 22–25 April 2012.
3. Vis, F.; Faulkner, S.; Parry, K.; Manuykhina, Y.; Evans, L. Twitpic-ing the riots: Analysing images
shared on Twitter during the 2011 UK riots. In: Twitter and Society; Weller, K., Bruns, A., Burgess, J.,
Mahrt, M., Puschmann, C., Eds.; Peter Lang: New York, NY, USA, 2013; pp. 385–398.
4. Kharroub, T.; Bas, O. Social media and protests: An examination of Twitter images of the 2011
Egyptian revolution. New Media Soc. 2015, 18, 1973–1992, doi:10.1177/1461444815571914.
5. Seltzer, E.K.; Jean, N.S.; Kramer-Golinkoff, E.; Asch, D.A.; Merchant, R.M. The content of social media’s
shared images about Ebola: A retrospective study. Public Health 2015, 129, 1273–1277,
doi:10.1016/j.puhe.2015.07.025.
6. Barthes, R. Rhetoric of the image. In: Image, Music, Text; Heath, S., Eds.; Hill and Wang: New York, NY,
USA, 1977; pp. 32–51.
7. Hochman, N.; Schwartz, R. Visualizing instagram: Tracing cultural visual rhythms. In Proceedings of
the workshop on Social Media Visualization (SocMedVis) in Conjunction with the Sixth International
AAAI Conference on Weblogs and Social Media (ICWSM–12), Dublin, Ireland, 4–7 June 2012.
8. Murthy, D.; Gross, A.; McGarry, M. Visual Social Media and Big Data. Interpreting Instagram Images
Posted on Twitter. Digit. Cult. Soc. 2016, 2, 113–134, doi:10.14361/dcs-2016-0208.
9. Tifentale, A.; Manovich, L. Selfiecity: Exploring photography and self-fashioning in social media. In:
Postdigital Aesthetics; Berry, D., Dieter, M., Eds.; Palgrave Macmillan: Basingstoke, UK, 2015, pp.
109–122.
10. Walsh, M.J.; Baker, S.A. The selfie and the transformation of the public–private distinction. Inform.
Commun. Soc. 2017, 20, 1185–1203.
11. Zappavigna, M. Social media photography: Construing subjectivity in Instagram images. Vis.
Communi.2016, 15, 271–292, doi:10.1177/1470357216643220.
12. Hummel O’Donnell, N. Storied lives on Instagram: Factors associated with the need for
personal-visual identity. In Proceedings of the Association for Education in Journalism and Mass
Communication National Conference, Minneapolis, Minnesota, 4–7 August 2016.
13. Idoiaga Mondragon, N.; Gil de Montes, L.; Valencia, J. Understanding an Ebola outbreak: Social
representations of emerging infectious diseases. J. Health Psychol. 2016, 1–10,
doi:10.1177/1359105315620294.
14. Gilman, S.L. AIDS and Syphilis: The Iconography of Disease. October 1987, 43, 87–107.
15. Kapp, J.M.; Hensel, B.; Schnoring, K.T. Is Twitter a forum for disseminating research to health policy
makers? Ann. Epidemiol. 2015, 25, 883–887, doi:10.1016/j.annepidem.2015.09.002.
16. Papacharissi, Z.; de Fatima Oliveira, M. Affective news and networked publics: The rhythms of news
storytelling on #Egypt. J. Commun. 2012, 62, 266–282, doi:10.1111/j.1460- 2466.2012.01630.x.
Societies 2019, 9, 12 18 of 18
17. Gabarron, E.; Serrano, J.A.; Wynn, R.; Lau, A.Y.S. Tweet Content Related to Sexually Transmitted
Diseases: No Joking Matter. J. Med. Internet Res. 2014, 16, e228.
18. Harris, J.; Mart, A.; Moreland-Russell, S.; Caburnay, C. Diabetes topics associated with engagement on
Twitter. Prev. Chron. Dis. 2015, 12, doi:10.5888/ pcd12.140402.
19. Heverin, T.; Zach, L. Microblogging for Crisis Communication: Examination of Twitter Use in
Response to a 2009 Violent Crisis. In Proceedings of the 7th International ISCRAM Conference, Seattle,
DC, USA, 2–5 May 2010.
20. Love, B.; Himelboim, I.; Holton, A.; Stewart, K. Twitter as a source of vaccination information: Content
drivers and what they are saying. Am. J. Infect Control 2013, 41, 568–570, doi:10.1016/j.ajic.2012.10.016.
21. Rodriguez-Morales, A.J.; Castaneda-Hernandez, D.M.; McGregor, A. What makes people talk about
Ebola on social media? A retrospective analysis of Twitter use. Travel Med. Infect Dis. 2015, 13, 100–101,
doi:10.1016/j.tmaid.2014.11.004.
22. Odlum, M.; Yoon, S. What can we learn about the Ebola outbreak from tweets? Am. J. Infect Control
2015, 43, 563–571, doi:10.1016/j.ajic.2015.02.023.
23. Lazard, A.J.; Scheinfeld, E.; Bernhardt, J.M.; Wilcox, G.B.; Suran, M. Detecting themes of public
concern: A text mining analysis of the Centers for Disease Control and Prevention’s Ebola live Twitter
chat. Am. J. Infect Control 2015, 43, 1109–1111, doi:10.1016/j.ajic.2015.05.025.
24. The Lancet. The medium and the message of Ebola. Lancet 2014, 384,
doi:10.1016/S0140-6736(14)62016-X.
25. Ungar, S. Hot crises and media reassurance: A comparison of emerging diseases and Ebola Zaire. Br. J.
Sociol. 1998, 49, 36–56.
26. Martinec, R.; Salway, A. A System for Image-Text Relations in New (And Old) Media. Vis. Commun.
2005, 337–371, doi:10.1177/1470357205055928.
27. Joffe, H.; Haarhoff, G. Representations of far-flung illnesses: The case of Ebola in Britain. Soc. Sci. Med.
2002, 54, 955–969, doi:10.1016/S0277-9536(01)00068-5.
28. Mitchel, W.J.T. Picture Theory. Essays on Verbal & Visual Representation; The University of Chicago Press:
Chicago: IL, USA, 1995.
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article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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