The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Measuring Emotional Contagion in Social Media
Open accessAbstract
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.
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Cites
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Twitter mood predicts the stock market
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Cited by
Social comparison on social networking sites
Verduyn, P., Gugushvili, N., Massar, K., Täht, K., & Kross, E. (2020) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2020.04.002 · Cited by 500
Disinformation and social bot operations in the run up to the 2017 French presidential election
Ferrara, E. (2017) · First Monday · doi:10.5210/fm.v22i8.8005 · Cited by 524
Self-citationBots with Feelings: Should AI Agents Express Positive Emotion in Customer Service?
Han, E., Yin, D., & Zhang, H. (2023) · Information Systems Research · doi:10.1287/isre.2022.1179 · Cited by 307
Nesi, J., Choukas-Bradley, S., & Prinstein, M. J. (2018) · Clinical Child and Family Psychology Review · doi:10.1007/s10567-018-0262-9 · Cited by 266
Emotional contagion: a brief overview and future directions
Herrando, C., & Constantinides, E. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.712606 · Cited by 249
“Since such content is often shared, the emotional valence of the information shared via social media is affected by the emotional content observed (Ferrara and Yang, 2015; Sciara et al., 2021).”
Background
Metadata
- Authors
- Ferrara, E., & Yang, Z.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-11-06
- Volume
- 10
- Issue
- 11
- Pages
- e0142390
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W1495512916
- Cited by
- 532
- References
- 68
- FWCI
- 14.54
- Type
- journal article
- Language
- en
- Open access
- gold
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