The blame game: Understanding blame assignment in social media
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Abstract (arXiv)
Cognitive and psychological studies on morality have proposed underlying linguistic and semantic factors. However, laboratory experiments in the philosophical literature often lack the nuances and complexity of real life. This paper examines how well the findings of these cognitive studies generalize to a corpus of over 30,000 narratives of tense social situations submitted to a popular social media forum. These narratives describe interpersonal moral situations or misgivings; other users judge from the post whether the author (protagonist) or the opposing side (antagonist) is morally culpable. Whereas previous work focuses on predicting the polarity of normative behaviors, we extend and apply natural language processing (NLP) techniques to understand the effects of descriptions of the people involved in these posts. We conduct extensive experiments to investigate the effect sizes of features to understand how they affect the assignment of blame on social media. Our findings show that aggregating psychology theories enables understanding real-life moral situations. Moreover, our results suggest that there exist biases in blame assignment on social media, such as males are more likely to receive blame no matter whether they are protagonists or antagonists.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Feb 2023.
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Citing papers per year: 2023: 2; 2024: 2.
4 papers.
- 2024SocialGaze: Improving the Integration of Human Social Norms in Large Language Modelscited by 4
- 2023Conversation Modeling to Predict Derailmentcited by 2
- 2024A Benchmark for Cross-Domain Argumentative Stance Classification on Social Mediacited by 1
- 2023Moral Sparks in Social Media Narrativescited by 0
- 2024A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media11 Oct 2024
- 2024SocialGaze: Improving the Integration of Human Social Norms in Large Language Models11 Oct 2024
- 2023Moral Sparks in Social Media Narratives30 Oct 2023
- 2023Conversation Modeling to Predict Derailment20 Mar 2023