Multiple Inputs and Hybrid Deep Learning Model to Identify Suspicious Posts on Social Media

With the rise of social media platforms and the vast amount of text content generated on these platforms, text data forensics has emerged as a new area of research that aims to identify suspicious activities by analyzing textual content. This study aims to identify potentially harmful content on social media by examining both the text and metadata of the posts. The metadata is fed into a feature selection method to select the most beneficial features. Using multiple inputs, a hybrid deep learning framework is proposed to classify Twitter posts as normal or suspicious. Suspicious text can be defined as any post that appears related to criminal activities or is unusual. This research would significantly contribute to the field of text data forensics by enhancing the identification of such suspicious texts. A comprehensive and recent dataset for text data forensics called CIC Truth Seeker Dataset 2023 was used to assess the effectiveness of the proposed framework. The proposed framework uses long short-term memory (LSTM) to process textual data and hybrid residual neural network (ResNet) and deep neural network (DNN) layers for metadata. The framework has shown promising results during its preliminary evaluations.