A Cognitively Grounded Bayesian Framework for Misinformation Susceptibility
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Abstract (arXiv)
In this (work in progress) paper, we present Bounded Pragmatic Listener (or BPL), a cognitively grounded Bayesian framework for modelling susceptibility to information disorder. BPL extends Rational Speech Act theory with three cognitively motivated bounds derived from the bounded rationality literature with a) a recursion depth bound (that emphasises working memory limits);b) a prior compression parameter (which is oriented at capturing information bottleneck); and c) an availability sample size (that operationalises importance sampling with saliency-weighted proposals). This allows us to test predictions about misinformation susceptibility, annotator disagreement, and the differential vulnerability to mis-, dis-, and mal-information as defined in the Information Disorder framework. We validate BPL on the LIAR and MultiFC benchmarks showcasing competitive veracity classification and experimental support for the depth-mismatch paradox.
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References · 9
- 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
- 2021A Survey on Automated Fact-Checkingcited by 378
- 2017"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detectioncited by 363
- 2019MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claimscited by 169
- 2018Automated Fact Checking: Task formulations, methods and future directionscited by 96
- 2020We Can Detect Your Bias: Predicting the Political Ideology of News Articlescited by 92
- 2021Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreementcited by 72
- 2018Predicting Factuality of Reporting and Bias of News Media Sourcescited by 71
- 2018Color naming reflects both perceptual structure and communicative needcited by 13
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