On the Characterization of Digital Trolls from Twitter Big Data - khudhair Abbas, Ahmed; Hassan Safi, Hayder; Hasan Taresh, Ali (International Journal of Psychosocial Rehabilitation, 2020)
Recently, Twitter has become one of the most common and effective social media tools in our lives. People use Twitter to share their opinions, feelings, and orientations, especially during political unrest or protests. In order to disrupt the protest operations on Twitter or to influence public opinion, electronic flies (trolls) are widely and effectively used. Accordingly, there is a need to find a method that can automatically and precisely detect these accounts and isolate them from Twitter. Moreover, detecting and characterizing these accounts becomes a significant task to reduce or mitigate its effect on the real general opinion. This paper presents an intensive analysis that can be utilized to effectively detect the troll accounts and isolate their bad effects from Twitter. We considered the public trolls account datasets published by Twitter, and we also gathered a new dataset from Twitter that includes tweets and users’ information from different countries to make a fair analysis for the trolls’ accounts. The results show that the suspicious activities of Twitter troll accounts can be used to detect most of these accounts automatically without using sentiment analysis and opinion mining techniques with an accuracy of 95%. To accomplish this task, we propose a set of robust and efficient features that can accurately characterize troll accounts with a relatively small number of features.