Wijaya, DerryAndy, AnietieKriz, RenoGuntuku, Sharath ChandraCallison-Burch, Chris2023-10-102023-10-102022-06-20Anietie Andy, Reno Kriz, Sharath Chandra Guntuku, Derry Tanti Wijaya, and Chris Callison-Burch. 2022. Did that happen? Predicting Social Media Posts that are Indicative of what happened in a scene: A case study of a TV show. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 7209–7214, Marseille, France. European Language Resources Association. https://aclanthology.org/2022.lrec-1.781https://hdl.handle.net/2144/46871While popular Television (TV) shows are airing, some users interested in these shows publish social media posts about the show. Analyzing social media posts related to a TV show can be beneficial for gaining insights about what happened during scenes of the show. This is a challenging task partly because a significant number of social media posts associated with a TV show or event may not clearly describe what happened during the event. In this work, we propose a method to predict social media posts (associated with scenes of a TV show) that are indicative of what transpired during the scenes of the show. We evaluate our method on social media (Twitter) posts associated with an episode of a popular TV show, Game of Thrones. We show that for each of the identified scenes, with high AUC’s, our method can predict posts that are indicative of what happened in a scene from those that are not-indicative. Based on Twitters policy, we will make the Tweeter ID’s of the Twitter posts used for this work publicly available.© European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0https://creativecommons.org/licenses/by-nc/4.0/Conference materials2023-03-27824191