Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources

Maria Glenski, Tim Weninger, Svitlana Volkova


Abstract
In the age of social news, it is important to understand the types of reactions that are evoked from news sources with various levels of credibility. In the present work we seek to better understand how users react to trusted and deceptive news sources across two popular, and very different, social media platforms. To that end, (1) we develop a model to classify user reactions into one of nine types, such as answer, elaboration, and question, etc, and (2) we measure the speed and the type of reaction for trusted and deceptive news sources for 10.8M Twitter posts and 6.2M Reddit comments. We show that there are significant differences in the speed and the type of reactions between trusted and deceptive news sources on Twitter, but far smaller differences on Reddit.
Anthology ID:
P18-2029
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
176–181
Language:
URL:
https://aclanthology.org/P18-2029
DOI:
10.18653/v1/P18-2029
Bibkey:
Cite (ACL):
Maria Glenski, Tim Weninger, and Svitlana Volkova. 2018. Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 176–181, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources (Glenski et al., ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/P18-2029.pdf