DeepCredNet: Empowering Credibility Detection in Online Media Posts with Robust Deep Neural Networks

Abstract

The rampant dissemination of misinformation and fake news through online media necessitates effective methods for detecting credibility. This study proposes a novel approach that employs a deep neural network (DNN) architecture, specifically Long Short-Term Memory (LSTM), to accurately assess the credibility of online media posts. The proposed LSTM-based DNN model leverages the inherent sequential nature of textual content and metadata associated with the posts. By capturing long-term dependencies and temporal dynamics, the model effectively learns intricate patterns and features crucial for credibility detection. A large labelled dataset comprising both credible and non-credible posts is employed to train the LSTM-based DNN. The input features encompass textual content, user information, and contextual details such as the post source and timing. The LSTM layers within the network enable the model to capture and retain relevant information over extended periods, enhancing its discriminative capabilities. Experimental evaluations validate the efficacy of the proposed approach, showcasing its ability to discern between credible and non-credible online media posts with high accuracy and robust performance. The real-time applicability of this method enables prompt credibility assessment, offering valuable support in combating misinformation and aiding users in making informed decisions while consuming online media.