Classifying Reasonability in Retellings of Personal Events Shared on Social Media: A Preliminary Case Study with /r/AmITheAsshole
Keywords:Subjectivity in textual data; sentiment analysis; polarity/opinion identification and extraction, linguistic analyses of social media behavior, Measuring predictability of real world phenomena based on social media, e.g., spanning politics, finance, and health, Organizational and group behavior mediated by social media; interpersonal communication mediated by social media, Trend identification and tracking; time series forecasting
AbstractPeople regularly share retellings of their personal events through social media websites to elicit feedback about the reasonability of their actions in the event's context. In this paper, we explore how learning approaches can be used toward the goal of classifying reasonability in personal retellings of events shared on social media. We collect 13,748 community-labeled posts from /r/AmITheAsshole, a subreddit in which Reddit users share retellings of personal events which are voted upon by community members. We build and evaluate a total of 21 machine learning models across seven types of models and three distinct feature sets. We find that our best-performing model can predict the reasonability of a post with an F1 score of .76. Our findings suggest that features derived from the post and author metadata were more predictive than simple linguistic features like the post sentiment and types of words used. We conclude with a discussion on the implications of our findings as they relate to sharing retellings of personal events on social media and beyond.
How to Cite
Haworth, E., Grover, T., Langston, J., Patel, A., West, J., & Williams, A. C. (2021). Classifying Reasonability in Retellings of Personal Events Shared on Social Media: A Preliminary Case Study with /r/AmITheAsshole. Proceedings of the International AAAI Conference on Web and Social Media, 15(1), 1075-1079. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/18133