A web forensic optimization framework for investigating false information on social media using the ForenOptiNet model

Today’s technological advancements in the field of digital media have resulted in the unprecedented transmission of information leading to unauthorized exploitation. Businesses use social media as the primary marketing platform. Considering the severity of spreading misinformation and fake news in our society due to false marketing by bogus businesses, there is a great need to demystify this propagation using web forensics-based frameworks. In order to increase consumer equity, the rapid spreading of malicious information makes it hard for users to differentiate between real and false information. This research intends to design an effective and adaptable framework for detecting false information campaign carried out by criminals affecting online social network (ONS). A novel ForenOptiNet model is designed and diverse data gathered from the Reddit and INFD dataset is used to train the suggested model. The Web Forensic-Based Investigation Optimization (WFBIO) algorithm provides a high accuracy classification of malicious content from the web. Moreover, the WFBIO framework enhances the robustness of the ForenOptiNet model and ensures that the proposed approach can effectively identifies misinformation and fake news to validate factual claims. Results of the simulation analysis provides a muti-level mechanism combining anomaly detection and ForenOptiNet model together outperforming other state-of the-art optimization algorithms trained against CNNs with SGD, Adagrad and AdaDelta. While these baselines yielded accuracies between 55 and 92%, our proposed model achieved highest accuracy of 99% accuracy with an effective front-end design integration.