Detecting Spam Using Machine Learning Techniques on Social Media Platforms
Keywords:
Machine learning, Social media platforms, Spam, Spam review detection, Spam reviewsAbstract
The rapid growth of social media platforms like Twitter has revolutionized information sharing but also paved the way for malicious activities such as spam reviews and fraudulent content. Spam on Twitter not only degrades user experience but also threatens the reliability of online opinions and marketing efforts. With an emphasis on detecting false or misleading reviews and promotional content, this review paper thoroughly examines the many machine learning approaches used for spam detection on Twitter. It is examined different learning models and detection frameworks by analyzing aspects such as feature selection (including user behavior, tweet content, hashtags, URLs, and follower-following patterns), data preprocessing, handling of imbalanced datasets, and performance evaluation criteria. The paper also highlights commonly used datasets for Twitter spam detection and identifies ongoing challenges such as evolving spam tactics, scarcity of labeled data, and evasion strategies used by spammers. The study outlines key areas for future research, including the development of real-time and adaptive detection systems, the use of semantic and contextual understanding, and the integration of cross-platform detection mechanisms to enhance spam filtering on social media platforms.
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