@article{xi_singh_2023, title={The Blame Game: Understanding Blame Assignment in Social Media}, volume={4}, ISSN={["2329-924X"]}, url={https://doi.org/10.1109/TCSS.2023.3261242}, DOI={10.1109/TCSS.2023.3261242}, abstractNote={Psychological studies on morality have proposed underlying linguistic and semantic factors. However, current empirical studies often lack the nuances and complexity of real life. This article examines how well the findings of prior studies generalize to a corpus of over 30000 narratives of tense social situations submitted to a popular social media forum. A poster describes interpersonal moral situations or misgivings; other users judge from the post whether the poster (protagonist) or an opposing side (antagonist) is morally culpable. 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 psychological theories enables understanding real-life moral situations. We also find evidence of bias blame assignment on social media, such as that males are likelier to receive blame no matter whether they are protagonists or antagonists.}, journal={IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS}, author={Xi, Ruijie and Singh, Munindar P. P.}, year={2023}, month={Apr} }