MISINFORMATION DETECTION IN THE SOCIAL MEDIA ERA

Abstract

As social media becomes the main way of getting information, the spread of misinformation is a serious and widespread problem. Misinformation can take many forms, such as text, video, and audio, and it can travel quickly through different platforms, affecting the quality and trustworthiness of the information that users access around the world. Misinformation can have negative effects on how people think, act, and interact, and it can even endanger social peace. This study aims to tackle the complex problem of misinformation by presenting a comprehensive approach that addresses various forms of deceptive content on social media with a focus on Twitter ( currently X). Twitter stands out as a dynamic and influential microblogging service that enables users to share real-time updates, news, and opinions in concise 280-character messages known as tweets. We introduce a hybrid deep learning model that incorporates Feature-based models at both tweet and user levels, complemented by pre-trained text embedding models such as Global Vectors (GloVe) and Universal Sentence Encoders (USE). Through careful evaluation on a real-world dataset, our approach proves effective in detecting textual misinformation. Recognizing the vital need to verify the reliability of information on social media, we propose a method to assess user credibility. Our solution involves evaluating the credibility of users based on their profiles to enhance the rumors detection model. This study proposes a novel mechanism for assessing a user’s credibility. Additionally, we extended our study capabilities to address the challenges posed by deceptive video content spread on social media using DeepFake technology. As the rapid advancement of deepfake technology threatens the integrity of audio and video content, we present a novel approach combining Optical Flow (OF) algorithms with a Convolutional Neural Network (CNN) to enhance deepfake video detection. This comprehensive strategy addresses the diverse challenges posed by misinformation, credibility assessment, and deepfake detection in the dynamic landscape of social media.