Anomaly Detection in Social Media Using Machine Learning

In today’s digital communication landscape, social media platforms like Twitter (X), Facebook, Instagram, and YouTube have emerged as influential instruments for global connectivity, instantaneous news dissemination, public expression, and business marketing. With billions of users engaging on a daily basis, these platforms produce an immense amount of user-generated content, encompassing text, images, videos, and interactions. Although social media presents extensive opportunities, it simultaneously fosters an environment conducive to various forms of abnormal and malicious activities, including spam, counterfeit accounts, orchestrated disinformation efforts, hate speech, phishing links, and cyberbullying. Identifying such anomalies in real-time poses a significant challenge due to the ever-changing nature of social media content and the vast scale of data produced. Traditional detection methods are often ineffective, labor-intensive, and lack scalability. Consequently, this project seeks to harness the capabilities of Machine Learning (ML) to create an intelligent system that can accurately identify anomalous behaviors or patterns on social media platforms with minimal human oversight.