Improving association discovery through multiview analysis of social networks

The rise of social networks has brought about a transformative impact on communication and the dissemination of information. However, this paradigm shift has also introduced many challenges in discerning valuable conversation threads amidst fake news, malicious accounts, background noise, and trolling. In this study, we address these challenges by focusing on propagating fake news labels. We evaluate the efficacy of community-based modeling in effectively addressing these challenges within the context of social network discussions using the state-of-the-art benchmark. Through a comprehensive analysis of millions of users engaged in discussions on a specific topic, we unveil compelling evidence demonstrating that community-based modeling techniques yield precision, recall, and accuracy levels comparable to those achieved by lexical classifiers. Remarkably, these promising results are achieved even without considering the textual content of tweets beyond the information conveyed by hashtags. Moreover, we explore the effectiveness of fusion techniques in tweet classification and underscore the superiority of a combined community and lexical approach, which consistently delivers the most robust outcomes and exhibits the highest performance measures. We illustrate this capability with specific network graphs constructed based on Twitter interactions related to the COVID-19 pandemic, showcasing the practicality and relevance of our proposed methodology. To demonstrate the excellent performance achieved with the fusion of modalities, we show an improvement of the combined lexical and community method that achieves up to 60% both for precision and recall measures.