  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.
