Analyzing Numerical Patterns in Twitter Data: Unveiling Fake and Bot Accounts During Telangana State Elections

Online Social Networks (OSNs) have become ubiquitous platforms for the dissemination of diverse content, en-compassing text, images, and videos. However, the proliferation of fake accounts poses a formidable challenge to the integrity of current OSN systems. Exploiting these fraudulent profiles, malicious actors distribute misleading information, ranging from deceptive surveys to fabricated reports of election rigging and false narratives about the government. Our proposal involves utilizing the latest developments in deep learning, namely in computer vision to address the widespread problem of phony accounts. namely, we suggest implementing an Artificial Neural Network (ANN) and certain Machine Learning methods. Through a series of experiments, our findings reveal a promising outcome, demonstrating superior accuracy and minimal loss when compared to prevalent learning algorithms in the realm of fake account classification. This research contributes to the ongoing discourse on enhancing the robustness of OSN systems against the propagation of misleading information through fake accounts.