Leveraging Socio-contextual Information in BERT for Fake Health News Detection in Social Media

Fake news is a major challenge in social media, particularly in the health domain where it can lead to severe consequences for both individuals and society as a whole. To contribute to combating this problem, we present a novel solution for improving the accuracy of detecting fake health news, utilizing a fine-tuned BERT model that integrates both user- and content-related socio-contextual information. Specifically, this information is combined with the textual content itself to form a socio-contextual input sequence for the BERT model. By fine-tuning such a model with respect to the health misinformation detection task, the resulting classifier can accurately predict the category to which each piece of content belongs, i.e., either "real health news" or "fake health news". We validate our solution through a series of experiments conducted on distinct publicly available datasets constituted by health-related tweets. These results illustrate the superiority of the proposed solution compared to the standard BERT baseline model and other advanced models. Indeed, they show that the integration of socio-contextual information in the detection process positively contributes to increasing the overall accuracy of the fake health news detection task. The study also suggests, in a preliminary way, how such information could be used for the explainability of the model itself.