Measuring Emotional Contagion in Social Media

By Emilio Ferrara, Zeya Yang. PLoS ONE, 2015

506 citations recorded by the source corpus.

Social media are used as main discussion channels by millions of individuals every day. The content individuals produce in daily social-media-based micro-communications, and the emotions therein expressed, may impact the emotional states of others. A recent experiment performed on Facebook hypothesized that emotions spread online, even in absence of non-verbal cues typical of in-person interactions, and that individuals are more likely to adopt positive or negative emotions if these are over-expressed in their social network. Experiments of this type, however, raise ethical concerns, as they require massive-scale content manipulation with unknown consequences for the individuals therein involved. Here, we study the dynamics of emotional contagion using a random sample of Twitter users, whose activity (and the stimuli they were exposed to) was observed during a week of September 2014. Rather than manipulating content, we devise a null model that discounts some confounding factors (including the effect of emotional contagion). We measure the emotional valence of content the users are exposed to before posting their own tweets. We determine that on average a negative post follows an over-exposure to 4.34% more negative content than baseline, while positive posts occur after an average over-exposure to 4.50% more positive contents. We highlight the presence of a linear relationship between the average emotional valence of the stimuli users are exposed to, and that of the responses they produce. We also identify two different classes of individuals: highly and scarcely susceptible to emotional contagion. Highly susceptible users are significantly less inclined to adopt negative emotions than the scarcely susceptible ones, but equally likely to adopt positive emotions. In general, the likelihood of adopting positive emotions is much greater than that of negative emotions.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b3434eceff25c9070cd150338620ac796ae8c3c5

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Citations in the corpus

Institutional isomorphism, negativity bias and performance information use by politicians: A survey experiment (2020, background): “Interestingly, there is even some indication of positivity bias (Ferrara and Yang 2015) in the case of the coercive pressure treatment, as respondents receiving the coercive positive treatment are more likely to use the information for analysing their overall municipal”
The Covert Advertising Recognition and Effects (CARE) model: Processes of persuasion in native advertising and other masked formats (2020, background): “…and Comisky 1978; O’Keefe and Jensen 2008), marketing (Meyers-Levy and Malaviya 1999) and information science (e.g. Yovits, Foulk, and Rose 1981; Ferrara and Yang 2015) over the past half century, and considerable exemplary scholarship in these areas has deepened our ability to predict how…”
Emotional contagion in organizational life (2018, background): “While research in this domain is still relatively nascent, extant work suggests that emotions can in fact spread virtually, both among teams and, more broadly, across entire social networking platforms (Cheshin et al., 2011; Del Vicario et al., 2016; Ferrara & Yang, 2015; Kramer et al., 2014).”
Disinformation and social bot operations in the run up to the 2017 French presidential election (2017, background): “…followers and more followees), the number of times a user has been added to a public list – human users are often considered more influential (Aral, & Walker, 2012) and their content more “contagious” (Kramer, et al., 2014; Ferrara & Yang, 2015a; Ferrara & Yang, 2015b; Mønsted, et al., 2017), etc.”
Bots increase exposure to negative and inflammatory content in online social systems (2018, unknown): “We previously illustrated [8] that the following indicators provide the strongest signals to separate humans from, in particular, political bots: (i) whether the public Twitter profile looks like the default one or it is customized (it requires human efforts to customize a profile, therefore bots a”
Social Comparison as the Thief of Joy: Emotional Consequences of Viewing Strangers’ Instagram Posts (2018, background): “Similarly, in a random sample of 3,800 Twitter users, posters who viewed relatively more positive tweets subsequently posted more positive tweets themselves, whereas posters who viewed more negative content posted more negative content themselves (Ferrara & Yang, 2015).”