Analyzing Political Parody in Social Media
Antonis Maronikolakis, Danae Sánchez Villegas, Daniel Preotiuc-Pietro, Nikolaos Aletras
Abstract
Parody is a figurative device used to imitate an entity for comedic or critical purposes and represents a widespread phenomenon in social media through many popular parody accounts. In this paper, we present the first computational study of parody. We introduce a new publicly available data set of tweets from real politicians and their corresponding parody accounts. We run a battery of supervised machine learning models for automatically detecting parody tweets with an emphasis on robustness by testing on tweets from accounts unseen in training, across different genders and across countries. Our results show that political parody tweets can be predicted with an accuracy up to 90%. Finally, we identify the markers of parody through a linguistic analysis. Beyond research in linguistics and political communication, accurately and automatically detecting parody is important to improving fact checking for journalists and analytics such as sentiment analysis through filtering out parodical utterances.- Anthology ID:
- 2020.acl-main.403
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4373–4384
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.403
- DOI:
- 10.18653/v1/2020.acl-main.403
- Cite (ACL):
- Antonis Maronikolakis, Danae Sánchez Villegas, Daniel Preotiuc-Pietro, and Nikolaos Aletras. 2020. Analyzing Political Parody in Social Media. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4373–4384, Online. Association for Computational Linguistics.
- Cite (Informal):
- Analyzing Political Parody in Social Media (Maronikolakis et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.403.pdf