TexTrolls: Identifying Russian Trolls on Twitter from a Textual Perspective
This addresses the challenge of identifying deceptive users who spread incitement and fake messages online, which is an incremental improvement in social media security.
The paper tackles the problem of detecting Russian troll accounts on Twitter by proposing a text-based approach that uses thematic and profiling features, achieving enhanced performance with profiling features performing best.
The online new emerging suspicious users, that usually are called trolls, are one of the main sources of hate, fake, and deceptive online messages. Some agendas are utilizing these harmful users to spread incitement tweets, and as a consequence, the audience get deceived. The challenge in detecting such accounts is that they conceal their identities which make them disguised in social media, adding more difficulty to identify them using just their social network information. Therefore, in this paper, we propose a text-based approach to detect the online trolls such as those that were discovered during the US 2016 presidential elections. Our approach is mainly based on textual features which utilize thematic information, and profiling features to identify the accounts from their way of writing tweets. We deduced the thematic information in a unsupervised way and we show that coupling them with the textual features enhanced the performance of the proposed model. In addition, we find that the proposed profiling features perform the best comparing to the textual features.