The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers

[EN] Users play a critical role in the creation and propagation of fake news online by consuming and sharing articles with inaccurate information either intentionally or unintentionally. Fake news are written in a way to confuse readers and therefore understanding which articles contain fabricated information is very challenging for non-experts. Given the di culty of the task, several fact checking websites have been developed to raise awareness about which articles contain fabricated information. As a result of those platforms, several users are interested to share posts that cite evidence with the aim to refute fake news and warn other users. These users are known as fact checkers. However, there are users who tend to share false information, who can be characterised as potential fake news spreaders. In this paper, we propose the CheckerOrSpreader model that can classify a user as a potential fact checker or a potential fake news spreader. Our model is based on a Convolutional Neural Network (CNN) and combines word embeddings with features that represent users' personality traits and linguistic patterns used in their tweets. Experimental results show that leveraging linguistic patterns and personality traits can improve the performance in di erentiating between checkers and spreaders.The work of the first author is supported by the SNSF Early Postdoc Mobility grant under the project Early Fake News Detection on Social Media, Switzerland (P2TIP2 181441). The work of Paolo Rosso is partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018- 096212-B-C31).Giachanou, A.; Ríssola, EA.; Ghanem, B.; Crestani, F.; Rosso, P. (2020). The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers. 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