A Comprehensive Survey on Anomaly Detection in Social Media Networks: Challenges, Methods, and Future Directions

Social media networks have developed into a rich source of user-generated material and interactions in the quickly changing digital age. The identification of abnormal behaviour, such as spam, fake news, cyberbullying, and organized disinformation efforts, has become a crucial task due to the overwhelming volume and velocity of data collected. An extensive overview of anomaly detection methods in social media networks is given in this study article. It examines the most recent approaches, stressing the underlying algorithms, benefits, and drawbacks of each. Along with examining the difficulties encountered in real-world applications-such as scalability, data heterogeneity, and the dynamic nature of social interactions-the study also covers a variety of anomalies unique to social media platforms. This survey attempts to provide a thorough road map for researchers and practitioners seeking to improve the security, reliability, and integrity of social media platforms by looking at recent developments and highlighting open research problems.