Echo Chambers: Emotional Contagion and Group Polarization on Facebook (1607.01032v1)

Abstract: 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. 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.

Echo Chambers: Emotional Contagion and Group Polarization on Facebook

This paper investigates the tendency of Facebook users to form echo chambers and its effects on emotional contagion and group polarization, focusing on communities centered around scientific content and conspiracy theories. Echo chambers are social groupings where individuals reinforce shared beliefs, often leading to increased polarization. The paper provides an in-depth analysis of how such communities evolve based on user engagement and emotional dynamics.

Methodology

The authors employ a robust methodological framework including growth models and sentiment analysis to explore the dynamics within science and conspiracy-related Facebook communities. The dataset comprises 73 public Italian Facebook pages with over a million comments collected from 2010 to 2014. These pages were divided into two categories based on their content: 34 pages promoting scientific information and 39 pages disseminating conspiracy theories.

Analysis of Growth Models

The paper first applies three growth models—the Gompertz model, the Logistic model, and the Log-logistic model—to the community size data. The intention is to capture the growth pattern of both science and conspiracy communities. Notably, the analysis reveals that both community types experience an initial rapid growth phase followed by a plateau, aligning well with the Gompertz and Logistic models, which is indicative of a saturation point in community size.

Emotional Dynamics

Beyond structural growth, the paper explores the emotional states influenced by community engagement. Sentiment within user comments was classified as positive, negative, or neutral through machine learning techniques. Findings indicate that more active users tend to shift toward a more negative emotional state over time. The negativity bias was notably increased among users with higher engagement levels, suggesting that active participation in these echo chambers correlates with more polarized emotional states.

Implications for Community Sentiment

The research further underscores the collective emotional atmosphere of these communities. Increased community activity correlates with a shift toward more negative sentiment, amplifying existing biases and promoting polarization. Particularly, conspiracy groups showed marked trends of decreasing sentiment polarity with increased activity, suggesting a homogenization of negative outlooks within these communities.

Conclusion and Implications

This paper contributes to an understanding of how echo chambers can magnify emotional polarization in online environments. The implications are substantial for designing interventions aimed at reducing misinformation spread on social media platforms. The results suggest that increased engagement within polarized communities may exacerbate confirmation bias, leading to deeper entrenchment of views. Such insights could inform future strategies for platform governance and the development of algorithms aimed at facilitating balanced information consumption.

The paper lays a formidable groundwork for further exploration into the mechanisms of emotional contagion and group polarization in digital social networks. Future research could extend these findings by examining other forms of content and varying sociocultural contexts, alongside exploring strategies for mitigating polarization and fostering more diverse informational environments. This research contributes to the broader computational social science field by illustrating the complex interplay between human emotional dynamics and digital information dissemination.