"This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders
arXiv:2202.04752 · doi:10.1145/3485447.3512264
Abstract
Individuals can be misled by fake news and spread it unintentionally without knowing it is false. This phenomenon has been frequently observed but has not been investigated. Our aim in this work is to assess the intent of fake news spreaders. To distinguish between intentional versus unintentional spreading, we study the psychological explanations of unintentional spreading. With this foundation, we then propose an influence graph, using which we assess the intent of fake news spreaders. Our extensive experiments show that the assessed intent can help significantly differentiate between intentional and unintentional fake news spreaders. Furthermore, the estimated intent can significantly improve the current techniques that detect fake news. To our best knowledge, this is the first work to model individuals' intent in fake news spreading.
arXiv commentAccepted to The ACM Web Conference (WWW) 2022
References in corpus (13)
- XGBoost: A Scalable Tree Boosting System
- Longformer: The Long-Document Transformer
- A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
- The spread of low-credibility content by social bots
- Automatic Detection of Fake News
- ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research
- Detection of Novel Social Bots by Ensembles of Specialized Classifiers
- What Types of COVID-19 Conspiracies are Populated by Twitter Bots?
- A Topic-Agnostic Approach for Identifying Fake News Pages
- Causal Understanding of Fake News Dissemination on Social Media
- MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation
- The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News
- SciLens: Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature Indicators