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

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. 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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“…Some of these visual images induce the intergenerational desire and the need to transform body images from an older to a newer one. This finding is consistent with that of Morin et al (2019), who in a study on 'text-image relationships in tweets: shaping the meanings of an epidemic' argued that the research proposes a four-category classification of text-image relationships.…”

“…Many studies concentrate on social functions involving images such as the rise of 'citizen journalists' (Murthy, 2011), the documentation and management of life's catastrophes (Terpstra et al, 2012) and political crisis (Vis et al, 2013), or they limit themselves to analyzing how images contribute to the discursive construction of an event (Kharoub & Bas, 2015;Seltzer et al, 2015). Morin et al (2019) note that the relationship between text and image is admittedly a very specific topic of research, however, it 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.…”

“…In the same vein, Paulusson and Harder (2014) Most studies have based their visual corpus on extractions of images coming from social networks. Such studies expose methodological difficulties (Hochman & Schwartz, 2012;Murthy, Gross & McGarry, 2016) or search traces of a new visual culture, especially through selfies (Morin, Mercier & Atlani-Duault, 2019;Tifentale & Manovich, 2015;Walsh & Baker, 2017), the art of exposing subjectivity (Zappavigna, 2016) or self-esteem enhancement (Hummel O'Donnel, 2016).…”

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