Enhancing Rumor Detection on Social Media Using Machine Learning and Empath Features

Abstract

In today's society, social media serves as a significant platform for information sharing, especially during news events when real-time updates are provided. Its accessibility, speed, and simplicity make it a valuable source of firsthand knowledge, enabling individuals to stay informed and connected, even during disasters. However, alongside its benefits, social media also harbors misinformation or rumors, which spread rapidly and can have detrimental effects. These rumors, unverified statements circulating on social platforms, can hinder the effectiveness of social media, particularly during crises, by disseminating false information and impeding real-time assistance efforts. Various approaches, including manual and automated classification models, have been employed to identify and address rumors on social media. While many existing methods focus on known rumor stories and predefined features, our research adopts a novel top-down approach that considers real-time tweets. We propose training multiple machine learning algorithms using an empath method to automatically extract additional features for classifying rumors and non-rumors. By incorporating these features, we aim to enhance the accuracy of rumor detection compared to previous methodologies, ultimately improving the efficacy of social media in disseminating reliable information.