2019/06/25 by K. Anoop, K, Anoop, P Deepak +3 · 1 citation
Computer Science · Social Sciences · #FOS: Computer and information sciences #Misinformation and Its Impacts #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI) #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1906.10365
openalex publication_date 2019/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Identifying misinformation is increasingly being recognized as an important computational task with high potential social impact. Misinformation and fake contents are injected into almost every domain of news including politics, health, science, business, etc., among which, the fakeness in health domain pose serious adverse effects to scare or harm the society. Misinformation contains scientific claims or content from social media exaggerated with strong emotion content to attract eyeballs. 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 to leverage emotion intensity lexicons to develop emotionized 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.