Echo Chambers: Emotional Contagion and Group Polarization on Facebook

By Michela Del Vicario, G. Vivaldo, Alessandro Bessi, Fabiana Zollo, Antonio Scala, Guido Caldarelli, Walter Quattrociocchi. Scientific Reports, 2016

539 citations recorded by the source corpus.

Recent findings showed that users on Facebook tend to select information that adhere to their system of beliefs and to form polarized groups – i.e., echo chambers. Such a tendency dominates information cascades and might affect public debates on social relevant issues. In this work we explore the structural evolution of communities of interest by accounting for users emotions and engagement. Focusing on the Facebook pages reporting on scientific and conspiracy content, we characterize the evolution of the size of the two communities by fitting daily resolution data with three growth models – i.e. the Gompertz model, the Logistic model, and the Log-logistic model. Although all the models appropriately describe the data structure, the Logistic one shows the best fit. Then, we explore the interplay between emotional state and engagement of users in the group dynamics. Our findings show that communities’ emotional behavior is affected by the users’ involvement inside the echo chamber. Indeed, to an higher involvement corresponds a more negative approach. Moreover, we observe that, on average, more active users show a faster shift towards the negativity than less active ones.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/72d5fe02b24f757e4d7669f47fc17774e7120439

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Citations in the corpus

Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for Identifying and Classifying Hate in Online News Media (2018, background): “Third, hateful comments spread hate and other negative emotions through emotional contagion (Kramer et al. 2014) and enhance the group polarization and echo chamber effects (Del Vicario et al., 2016).”
Fake News Detection on Social Media: A Data Mining Perspective (2017, background): “, by journalists) to a more disinter-mediated way [19].”
Online Hate Ratings Vary by Extremes: A Statistical Analysis (2019, background): “Online hatefulness has been found to be a phenomenon that is divisive [4], with difficulties in maintaining and assessing grey areas.”
FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media (2018, background): “Fake news dissemination processes tend to form an echo chamber cycle, highlighting the value of extracting network-based features to represent these types of network patterns for fake news detection (Del Vicario et al. 2016).”
The echo chamber effect on social media (2021, background): “, the tendency to seek information adhering to preexisting opinions) may explain the emergence of echo chambers on social media (1, 17, 21, 22).”