IntelliTweet: A Multifaceted Feature Approach to Detect Malicious Tweets

Twitter faces an ongoing issue with malicious tweets from deceptive accounts engaged in phishing, scams, and spam, negatively impacting the overall Twitter user experience. In response to growing security concerns, various machine learning-based methods have been deployed to detect and analyze these malicious activities. However, the evolving nature of the threats and tactics used by malicious actors cast doubts on the effectiveness of previously employed techniques. These methods often encounter challenges in addressing URL obfuscation techniques and managing false positive predictions. In this paper, we present “IntelliTweet”, an innovative solution designed to comprehend tweet content and accurately identify malicious tweets. This is achieved by incorporating a combination of contextual and content-based features, surpassing the use of conventional features alone. IntelliTweet takes a holistic approach that includes URL analysis, sentiment analysis, Twitter user analysis, and TFIDF-based content analysis, all working in tandem to enhance malicious tweet detection. For this system, our evaluation strategy places emphasis on reducing false positives while maintaining high precision. Through comparative experiments, we have demonstrated that IntelliTweet effectively counters URL obfuscation techniques, is robust, and minimizes the false positive rate. The system achieved a 98.38% precision, a 97.54% f-measure, and yielded a false positive rate of 0.14.