A scalable Aumann-Shapley attribution method for million-agent systems reveals that small-scale samples structurally misattribute emergence under nonlinear macro indicators, as shown by the Attribution Scaling Bias theorem.
A.et al.Exposure to opposing views on social media can increase political polarization.Proceedings of the National Academy of Sciences115, 9216–9221 (2018)
7 Pith papers cite this work. Polarity classification is still indexing.
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BiAxisAudit measures LLM bias on two axes—across-prompt sensitivity via factorial grids and within-response divergence via split coding—revealing that task format explains as much variance as model choice and that 63.6% of bias signals appear in only one layer.
Republican posts are more toxic than Democratic ones, but Democratic content draws more toxic replies due to higher volume of Republican cross-partisan engagement.
An AI system that elicits personal experiences and visualizes policy support increased perceived legitimacy and perspective-taking in collective decisions despite unfavorable outcomes.
Attitude-congruent AI dialogues reduce immediate affective and opinion polarization more than incongruent ones, while incongruent dialogues increase cognitive trait empathy over two weeks.
LLMs fail to emulate human belief dynamics: they mismatch initial distributions and show higher conformity than humans in network interactions.
A heterogeneous ensemble of XLM-RoBERTa-large and mDeBERTa-v3-base with independent task modeling and class weighting is reported as effective for multilingual, multicultural, and multievent online polarization detection.
citing papers explorer
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Attributing Emergence in Million-Agent Systems
A scalable Aumann-Shapley attribution method for million-agent systems reveals that small-scale samples structurally misattribute emergence under nonlinear macro indicators, as shown by the Attribution Scaling Bias theorem.
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BiAxisAudit: A Novel Framework to Evaluate LLM Bias Across Prompt Sensitivity and Response-Layer Divergence
BiAxisAudit measures LLM bias on two axes—across-prompt sensitivity via factorial grids and within-response divergence via split coding—revealing that task format explains as much variance as model choice and that 63.6% of bias signals appear in only one layer.
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"F*** You Biden": Cross-Partisan Electoral Toxicity on X
Republican posts are more toxic than Democratic ones, but Democratic content draws more toxic replies due to higher volume of Republican cross-partisan engagement.
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AI and Collective Decisions: Strengthening Legitimacy and Losers' Consent
An AI system that elicits personal experiences and visualizes policy support increased perceived legitimacy and perspective-taking in collective decisions despite unfavorable outcomes.
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Divergent Paths to Depolarization: Dialogue Design Determines the Prosocial Benefits of AI-Assisted Political Argumentation
Attitude-congruent AI dialogues reduce immediate affective and opinion polarization more than incongruent ones, while incongruent dialogues increase cognitive trait empathy over two weeks.
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Can LLMs Emulate Human Belief Dynamics?
LLMs fail to emulate human belief dynamics: they mismatch initial distributions and show higher conformity than humans in network interactions.
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YEZE at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization via Heterogeneous Ensembling
A heterogeneous ensemble of XLM-RoBERTa-large and mDeBERTa-v3-base with independent task modeling and class weighting is reported as effective for multilingual, multicultural, and multievent online polarization detection.