Misinformation Spread in Social Media

The pervasive influence of social media necessitateseffective strategies to combat the rapid dissemination of misinformation. This paper delves into the intricate task of rumor detection on Twitter, employing a multifaceted approach. The authorsleverage the capabilities of classical machine learning models, like SVM and Random Forests, to establish a baseline. Expanding the scope, the authors explore the potential of Artificial Neural Networks, leveraging their capacity to capture nuanced patterns in data. Additionally, they employ advanced transformer-based models like BERT and RoBERTa to enhance the understanding of semantic context. The study encompasses thorough experimentation with various text vectorization techniques, dimensionality reduction methods such as LDA, and comprehensive model evaluations. The findings reveal the supremacy of the BERT model, achieving an accuracy of 90.05%. This comprehensive analysis contributes valuable insights to the ongoing discourse on mitigating the impact of misinformation in the realm of social media.