Summary of Paying Attention to Deflections: Mining Pragmatic Nuances For Whataboutism Detection in Online Discourse, by Khiem Phi et al.
Paying Attention to Deflections: Mining Pragmatic Nuances for Whataboutism Detection in Online Discourse
by Khiem Phi, Noushin Salek Faramarzi, Chenlu Wang, Ritwik Banerjee
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the issue of whataboutism in quantitative NLP research, exploring its role in disrupting narratives and sowing distrust. The authors create new datasets from Twitter and YouTube to study overlaps and distinctions between whataboutism, propaganda, and the tu quoque fallacy. They also introduce a novel method using attention weights for negative sample mining, achieving significant improvements over previous state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Whataboutism is a tool used to disrupt narratives and spread misinformation. This paper studies how it’s used on social media platforms like Twitter and YouTube. The researchers created new datasets to understand whataboutism better and found that it’s often mixed with other tactics like propaganda. They developed a new way to detect whataboutism using special computer algorithms, which worked much better than previous methods. |
Keywords
» Artificial intelligence » Attention » Nlp
