TChecker: A Content Enrichment Approach for Fake News Detection on Social Media

Abstract

The spread of fake news on social media continues to be one of the main challenges facing internet users, prohibiting them from discerning authentic from fabricated pieces of information. Hence, identifying the veracity of the content in social posts becomes an important challenge, especially with more people continuing to use social media as their main channel for news consumption. Although a number of machine learning models were proposed in the literature to tackle this challenge, the majority rely on the textual content of the post to identify its veracity, which poses a limitation to the performance of such models, especially on platforms where the content of the users’ post is limited (e.g., Twitter, where each post is limited to 140 characters). In this paper, we propose a deep-learning approach for tackling the fake news detection problem that incorporates the content of both the social post and the associated news article as well as the context of the social post, coined TChecker. Throughout the experiments, we use the benchmark dataset FakeNewsNet to illustrate that our proposed model (TChecker) is able to achieve higher performance across all metrics against a number of baseline models that utilize the social content only as well as models combining both social and news content.

Keywords

• fake news
• BERT
• BERTweet
• BiLSTM
• social media