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Modeling And Measuring Expressed (Dis)belief In (Mis)information.pdf

Jiangetal2020ICWSM.pdf
Preview of Modeling and Measuring Expressed (Dis)belief in (Mis)information
🔗 Source: flanagin.faculty.comm.ucsb.edu
📊 Size: 485 KB
📄 Pages: 12 pages
⬇️ Downloads: 132

Summary

We collect a sample of tweets in response to (mis)information and annotate them with (dis)belief labels. Using lexicon-based methods, we explore the dataset and find differences in word usage between tweets expressing (dis)belief and others. We then experiment with classification models, including linear models with lexicon-derived features and state-of-the-art neural transfer-learning models (BERT, XLNet, and RoBERTa). Under a domain-specific thresholding strategy for unbiasedness, the best-performing classifier achieves macro-F1 scores around 0.86 for predicting disbelief and 0.80 for belief. We apply the classifier to a large, unlabeled dataset of social media comments in response to (mis)information and find that for true/mixed/false claims, 12%/14%/15% of comments express disbelief and 26%/21%/20% of comments express belief. We also find an extremely slight time effect of falsehood awareness, a positive effect of fact-checks on altering the prevalence of (dis)belief, and differences in (dis)belief expressed across three mainstream social media platforms (Facebook, Twitter, and YouTube).

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Description

Researchers propose using social media comments to model and measure public belief in online misinformation, aiming to quantify susceptibility and mitigate potential societal impacts.

Technical Information

  • File Format: PDF
  • File Size: 485 KB
  • Pages: 12
  • Language: EN
  • Total Downloads: 132
  • Last Updated: 20 hours ago

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