Calls to Action on Social Media: Detection, Social Impact, and Censorship Potential

Anna Rogers, Olga Kovaleva, Anna Rumshisky

Abstract
Calls to action on social media are known to be effective means of mobilization in social movements, and a frequent target of censorship. We investigate the possibility of their automatic detection and their potential for predicting real-world protest events, on historical data of Bolotnaya protests in Russia (2011-2013). We find that political calls to action can be annotated and detected with relatively high accuracy, and that in our sample their volume has a moderate positive correlation with rally attendance.
Anthology ID:
D19-5005
Volume:
Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Anna Feldman, Giovanni Da San Martino, Alberto Barrón-Cedeño, Chris Brew, Chris Leberknight, Preslav Nakov
Venue:
NLP4IF
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
36–44
Language:
URL:
https://aclanthology.org/D19-5005
DOI:
10.18653/v1/D19-5005
Bibkey:
Cite (ACL):
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2019. Calls to Action on Social Media: Detection, Social Impact, and Censorship Potential. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 36–44, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Calls to Action on Social Media: Detection, Social Impact, and Censorship Potential (Rogers et al., NLP4IF 2019)
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PDF:
https://preview.aclanthology.org/naacl24-info/D19-5005.pdf
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