Fake News Detection using Hashtag Context

Abstract

The proliferation of social media platforms has resulted in an exponential increase in user-generated content, facilitating the rapid and widespread dissemination of information. However, this ease of sharing content has also paved the way for the spread of false or misleading information, commonly known as fake news, which can have harmful effects on society. Existing studies in the literature rely on content in source posts, social interaction networks, and external evidence to verify the authenticity of the posts. However, studies in the literature fails to detect fake news in the following case. (i) Sparsity and limited words in social media posts heavily affect the performance of content-based methods. (ii) Social interaction-based methods require a huge social interaction network for a given source post, which is easily unavailable for every social media post. (iii) Social media discussions sometimes precede or surpass mainstream media reporting and information from external sources such as Knowledge Base and Wikipedia. Consequently, in such circumstances, getting external information that will help verify the authenticity of social media posts is not readily available. To address the above-mentioned limitations, this study proposes Hashtag Context-aware Fake News Detection (HCFND). Our proposed model, HCFND, leverages information posted under the hashtags mentioned in the source post and relevant posts extracted from named entities mentioned in the source post as external sources of information from the community with interest in similar topics. The extraction of external information from posts under relevant hashtags and profiles mentioned in source tweets enables the HCFND to cross-reference the content of the source post with data from communities sharing similar interests, thereby facilitating the verification of the authenticity of social media posts. We evaluate the performances of the proposed model on three publicly available benchmark datasets. The results indicate that our proposed model outperforms existing state-of-the-art methods in the literature.