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Impact of Intergroup Interactions on Religious Polarization in India during COVID-19 Discourse

Core Concepts
The authors explore how intergroup interactions impact religious polarization during significant events, finding that interactions can both reduce and increase polarization depending on the context. They introduce a new measure, Group Conformity Score (GCS), to assess polarization between religious groups.
The study examines the impact of intergroup interactions on religious polarization among Indian Twitter users discussing COVID-19. It finds that interactions can decrease or increase polarization based on the event context. The research highlights the importance of understanding group conformity and the role of intergroup interactions in shaping religious polarization. The content delves into the methodology used to infer religion from usernames, measure polarization using contextualized embeddings, and analyze topics discussed during COVID-19 events. The study also explores the effects of intergroup interaction on changes in Group Conformity Score (GCS) across different events. Additionally, it discusses the Oaxaca-Blinder decomposition to understand how differences in treatment effects across religious groups are influenced by topics and emotions. Overall, the research provides valuable insights into how intergroup interactions influence religious polarization on social media platforms during critical events, shedding light on the dynamics of group conformity and its implications for understanding societal divisions.
We compile data on nearly 700,000 Indian Twitter users engaging in COVID-19-related discourse during 2020. The dataset comprises over 132 million English language tweets from more than 20 million unique users. Muslims comprise 14% of the total population in India while Hindus make up 80%. The study uses a pre-trained all-mpnet-base-v2 model for sentence embeddings. The researchers identify seven highly discussed events based on tweet counts for analysis.
"We find that for political and social events, intergroup interactions reduce polarization." "During communal events, intergroup interactions can increase group conformity." "The results show that the dynamics of religious polarization are sensitive to context."

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by Rochana Chat... at 03-12-2024
Bridging or Breaking

Deeper Inquiries

How do cultural differences influence the impact of intergroup interactions on religious polarization?

Cultural differences play a significant role in shaping the impact of intergroup interactions on religious polarization. In the context provided, where Hindus and Muslims are the two main religious groups, their distinct cultural backgrounds can lead to varying responses to intergroup contact. Communication Styles: Cultural norms dictate communication styles, which can affect how individuals from different groups interact with each other. For example, Hindus and Muslims may have different approaches to conflict resolution or expressing opinions, leading to misunderstandings during intergroup interactions. Perceptions of Outgroups: Cultural beliefs and values shape perceptions of outgroups. Stereotypes and prejudices rooted in culture can influence how individuals from one group view members of another group during intergroup interactions. Historical Context: Historical events and narratives embedded in culture can fuel tensions between religious groups. Past conflicts or grievances may resurface during intergroup contact, intensifying polarization. Social Norms: Cultural norms regarding social hierarchy, gender roles, or authority figures can impact power dynamics within intergroup interactions. These dynamics can either facilitate understanding between groups or exacerbate existing divisions. Values and Beliefs: Core cultural values such as collectivism vs individualism, egalitarianism vs hierarchy, or secularism vs religiosity can shape attitudes towards diversity and inclusivity within society. In summary, cultural differences contribute to the complexity of intergroup interactions by influencing communication patterns, perceptions of outgroups, historical tensions' impacts on responses to contact situations.

How might historical tensions between majority and minority groups affect their responses to intergroup contact?

Historical tensions between majority and minority groups significantly influence their responses to intergroup contact in various ways: Trust Issues: Years of conflict or discrimination based on historical events create deep-rooted mistrust between these groups which hinders open dialogue during interaction. 2 .Power Dynamics: Historical oppression experienced by minority groups often leads them feeling marginalized when interacting with the majority group who holds more societal power. 3 .Emotional Baggage: Past traumas associated with historical injustices may evoke strong emotions like anger or resentment among both majority and minority group members during discussions. 4 .Identity Preservation: Minority communities may feel compelled to protect their identity due to past suppression which could manifest as resistance towards assimilation into mainstream culture through interaction. 5 .Stereotyping: Preconceived notions formed over time due to historical biases might lead both sides into stereotyping each other's intentions during conversations thereby hindering effective communication.

What potential biases could arise from inferring religion from usernames in social media data?

Several potential biases could arise from inferring religion from usernames in social media data: 1 .Simplification Bias: Assuming that a user's religion is accurately reflected by their username oversimplifies complex identities that cannot be fully captured by a single label. 2 .Confirmation Bias: Researchers might subconsciously interpret user behavior based on inferred religion leading them only focusing on information confirming pre-existing stereotypes about certain religions. 3 - Incorrect Assumptions: - Users may choose usernames for reasons unrelatedto their actual beliefs (e.g., satire,sarcasm)leadingto incorrect conclusions abouttheirreligious affiliation 4 - LackofDiversity: - Inferringreligionfromusernamesmay not capturethe full spectrumofreligiousdiversitywithin agroup,resultingin biased representationsandgeneralizations 5 - AlgorithmicBias: - The algorithm used for inferringreligionmightbebiasedtowards specific namesor cultures,resultingin inaccurate classificationsfor userswith lesscommonor non-traditionalnames