What makes people talk about Ebola on social media? A retrospective analysis of Twitter use

Rodríguez-Morales, Alfonso J.; Castañeda-Hernández, Diana M.; McGregor, Alastair

doi:10.1016/j.tmaid.2014.11.004

Cited by 28 publications

(20 citation statements)

References 5 publications

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“…Outside the intense transmission nations, most of the world witnessed the outbreak vicariously as portrayed in media accounts [20, 23•, 95–99]. In fact, in the USA, the rampant “contagion” of Ebola fear [23•] occurred in the virtual absence of Ebola disease transmission risks [2•, 3].…”

Section: Fear-related Behaviors In the 2013–2106 Evd Outbreakmentioning

confidence: 99%

The Role of Fear-Related Behaviors in the 2013–2016 West Africa Ebola Virus Disease Outbreak

Shultz

Cooper

Baingana

et al. 2016

Curr Psychiatry Rep

276

239

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The 2013–2016 West Africa Ebola virus disease pandemic was the largest, longest, deadliest, and most geographically expansive outbreak in the 40-year interval since Ebola was first identified. Fear-related behaviors played an important role in shaping the outbreak. Fear-related behaviors are defined as “individual or collective behaviors and actions initiated in response to fear reactions that are triggered by a perceived threat or actual exposure to a potentially traumatizing event. FRBs modify the future risk of harm.” This review examines how fear-related behaviors were implicated in (1) accelerating the spread of Ebola, (2) impeding the utilization of life-saving Ebola treatment, (3) curtailing the availability of medical services for treatable conditions, (4) increasing the risks for new-onset psychological distress and psychiatric disorders, and (5) amplifying the downstream cascades of social problems. Fear-related behaviors are identified for each of these outcomes. Particularly notable are behaviors such as treating Ebola patients in home or private clinic settings, the “laying of hands” on Ebola-infected individuals to perform faith-based healing, observing hands-on funeral and burial customs, foregoing available life-saving treatment, and stigmatizing Ebola survivors and health professionals. Future directions include modeling the onset, operation, and perpetuation of fear-related behaviors and devising strategies to redirect behavioral responses to mass threats in a manner that reduces risks and promotes resilience.

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Section: Fear-related Behaviors In the 2013–2106 Evd Outbreakmentioning

confidence: 99%

The Role of Fear-Related Behaviors in the 2013–2016 West Africa Ebola Virus Disease Outbreak

Shultz

Cooper

Baingana

et al. 2016

Curr Psychiatry Rep

276

239

View full text Add to dashboard Cite

show abstract

“…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][18][19][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.…”

Section: Ebola and Social Mediamentioning

confidence: 99%

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

2019

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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. 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 s...

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“…In the context of the recent Ebola epidemic, most studies were located in the first and third categories. To begin, some have suggested that social media could be used as an epidemiological tool, allowing health authorities to predict and monitor the Cult Med Psychiatry (2020) 44:56-79 57 propagation of the disease (Liaquat et al 2016;Rodriguez-Morales, Castañeda-Hernández and Mcgregor 2015). Secondly, some studies also analyzed social media to better understand reasons for misinterpretation of the Ebola disease, with a focus on misconceptions and false information circulating during the Ebola epidemic (Alnemer et al 2015;Jin et al 2014;Oyeyemi, Gabarron, and Wynn 2014).…”

Section: Introductionmentioning

confidence: 99%

Ebola and Localized Blame on Social Media: Analysis of Twitter and Facebook Conversations During the 2014–2015 Ebola Epidemic

Roy

Moreau

Rousseau

et al. 2019

Cult Med Psychiatry

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This study aimed to analyze main groups accused on social media of causing or spreading the 2014-2016 Ebola epidemic in West Africa. In this analysis, blame is construed as a vehicle of meaning through which the lay public makes sense of an epidemic, and through which certain classes of people become ''figures of blame''. Data was collected from Twitter and Facebook using key word extraction, then categorized thematically. Our findings indicate an overall proximate blame tendency: blame was typically cast on ''near-by'' figures, namely national governments, and less so on ''distant'' figures, such as generalized figures of otherness (''Africans'', global health authorities, global elites). Our results also suggest an evolution of online blame. In the early stage of the epidemic, blame directed at the affected populations was more prominent. However, during the peak of the outbreak, the increasingly perceived threat of inter-continental spread was accompanied by a progressively proximal blame tendency, directed at figures with whom the social media users had pre-existing biopolitical frustrations. Our study proposes that pro-active and on-going analysis of blame circulating in social media can usefully help to guide communications strategies, making them more responsive to public perceptions.

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What makes people talk about Ebola on social media? A retrospective analysis of Twitter use

Cited by 28 publications

References 5 publications

The Role of Fear-Related Behaviors in the 2013–2016 West Africa Ebola Virus Disease Outbreak

The Role of Fear-Related Behaviors in the 2013–2016 West Africa Ebola Virus Disease Outbreak

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

Ebola and Localized Blame on Social Media: Analysis of Twitter and Facebook Conversations During the 2014–2015 Ebola Epidemic

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