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Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization

arXiv 2402.11895 ↗First posted 19 Feb 2024cs.SI cs.CL physics.soc-phcited by 0

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Abstract (arXiv)

While exposure to diverse viewpoints may reduce polarization, it can also have a backfire effect and exacerbate polarization when the discussion is adversarial. Here, we examine the question whether intergroup interactions around important events affect polarization between majority and minority groups in social networks. We compile data on the religious identity of nearly 700,000 Indian Twitter users engaging in COVID-19-related discourse during 2020. We introduce a new measure for an individual's group conformity based on contextualized embeddings of tweet text, which helps us assess polarization between religious groups. We then use a meta-learning framework to examine heterogeneous treatment effects of intergroup interactions on an individual's group conformity in the light of communal, political, and socio-economic events. We find that for political and social events, intergroup interactions reduce polarization. This decline is weaker for individuals at the extreme who already exhibit high conformity to their group. In contrast, during communal events, intergroup interactions can increase group conformity. Finally, we decompose the differential effects across religious groups in terms of emotions and topics of discussion. The results show that the dynamics of religious polarization are sensitive to the context and have important implications for understanding the role of intergroup interactions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 76 references that are in the map. First posted on arXiv 19 Feb 2024.

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13 of 76 references.

References · 13

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  4. 2019Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootingscited by 67
  5. 2021TwiBot-20: A Comprehensive Twitter Bot Detection Benchmarkcited by 51
  6. 2022TwiBot-22: Towards Graph-Based Twitter Bot Detectioncited by 44
  7. 2019Discovering Polarized Communities in Signed Networkscited by 20
  8. 2017A Long-Term Analysis of Polarization on Twittercited by 17
  9. 2017Minimizing Polarization and Disagreement in Social Networkscited by 16
  10. 2015Measuring Political Polarization: Twitter shows the two sides of Venezuelacited by 7
  11. 2020It's All in the Name: A Character Based Approach To Infer Religioncited by 5
  12. 2022Non-Polar Opposites: Analyzing the Relationship Between Echo Chambers and Hostile Intergroup Interactions on Redditcited by 4
1 more
  1. 2021Divided We Rule: Influencer Polarization on Twitter During Political Crises in Indiacited by 2

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