  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.
