Authors: Reeta Bourasi, Dr. Sakshi Rai
Abstract: The importance of social media posts has been increased in our daily life activities. The wide distribution of different rumors and misinformation has affected many business organizations. The wide spread of rumors on social networking sites has brought many social, economic, personal, and political problems throughout the world. False information distorts public perceptions, undermines corporate reputations, disrupts communities, diminishes governmental and business integrity, and poses a significant threat to societal stability. False information affects public perception, harms corporate reputations, and threatens societal stability. Previous machine learning methods like Random-Forest and Gradient-Boosting have been used to detect rumors. However, the proposed hybrid model employs a Convolutional Neural Network combined with Bidirectional Long Short-Term Memory and Gated Recurrent Unit (CNN-BiLSTM and CNN-BiGRU) for classifying social media posts. This paper proposes an advanced hybrid deep learning-based approach that targets early rumor detection on Twitter and Facebook, achieving an accuracy of 98% – 99%, which surpasses baseline machine learning models.
