Triaging Twitter Users: An Exploratory Visual Analytics System
Abstract
Twitter is one of the most popular microblogging and social networking services. Many people from a wide range of backgrounds use Twitter to contribute their thoughts on different topics through postings, known as ``tweets”. Analysts collect and analyze tweets to extract knowledge. To rely on tweets, it is crucial to assess Twitter users’ credibility. In recent years, researchers have proposed various techniques, especially data analytics models, for evaluating Twitter users and analyzing their behaviour; however, these techniques do not engage analysts in the process, leading to a lack of understanding and trust in results. In this thesis, an exploratory visual analytics system is designed and implemented to help with triaging Twitter users. To this end, the system can leverage analysts’ expertise and knowledge through interactive visualization to assist them in understanding the underlying information within data. Subsequently, a case study demonstrates the capabilities of the system in identifying Twitter users.