Abstract
Twitter is one of the most popular micro-blogging social media platforms that has millions of users. Due to its popularity, Twitter has been targeted by different attacks such as spreading rumors, phishing links, and malware. Tweet-based botnets represent a serious threat to users as they can launch large-scale attacks and manipulation campaigns. To deal with these threats, big data analytics techniques, particularly shallow and deep learning techniques have been leveraged in order to accurately distinguish between human accounts and tweet-based bot accounts. In this paper, we discuss existing techniques, and provide a taxonomy that classifies the state-of-the-art of tweet-based bot detection techniques. We also describe the shallow and deep learning techniques for tweet-based bot detection, along with their performance results. Finally, we present and discuss the challenges and open issues in the area of tweet-based bot detection.