@inproceedings{maronikolakis-etal-2020-analyzing,
title = "Analyzing Political Parody in Social Media",
author = "Maronikolakis, Antonis and
S{\'a}nchez Villegas, Danae and
Preotiuc-Pietro, Daniel and
Aletras, Nikolaos",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.403",
doi = "10.18653/v1/2020.acl-main.403",
pages = "4373--4384",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Analyzing Political Parody in Social Media
%A Maronikolakis, Antonis
%A Sánchez Villegas, Danae
%A Preotiuc-Pietro, Daniel
%A Aletras, Nikolaos
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F maronikolakis-etal-2020-analyzing
%X 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.
%R 10.18653/v1/2020.acl-main.403
%U https://aclanthology.org/2020.acl-main.403
%U https://doi.org/10.18653/v1/2020.acl-main.403
%P 4373-4384
Markdown (Informal)
[Analyzing Political Parody in Social Media](https://aclanthology.org/2020.acl-main.403) (Maronikolakis et al., ACL 2020)
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.