Abstract
Identifying fake news is increasingly being recognized as an important computational task with high potential social impact. Misinformation is routinely injected into almost every domain of news including politics, health, science, business, etc., among which, the fake news in the health domain poses serious risk and harm to health and well-being in modern societies. In this paper, we consider the utility of the affective character of news articles for fake news identification in the health domain and present evidence that emotion cognizant representations are significantly more suited for the task. We outline a technique by leveraging emotion intensity lexicons to develop emotion-amplified text representations, and evaluate the utility of such a representation for identifying fake news relating to health in various supervised and unsupervised scenarios. The consistent and significant empirical gains that we observe over a range of technique types and parameter settings establish the utility of the emotional information in news articles, an often overlooked aspect, for the task of misinformation identification in the health domain.
Original language | English |
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Title of host publication | IDEAS 2020 Conference: Proceedings |
Publisher | Association for Computing Machinery |
ISBN (Print) | 978-1-4503-7503-0 |
DOIs | |
Publication status | Published - 2020 |
Event | 24th International Database Engineering & Applications Symposium - Duration: 12 Aug 2020 → 18 Aug 2020 http://confsys.encs.concordia.ca/IDEAS/ideas20/ideas20.php |
Conference
Conference | 24th International Database Engineering & Applications Symposium |
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Abbreviated title | IDEAS 2020 |
Period | 12/08/2020 → 18/08/2020 |
Internet address |