The Detection of Twitter Trolls Interventions Using Machine Learning Algorithms
Keywords:
Disinformation, Troll activities detection, Troll farms, amplifying political campaignsAbstract
Media campaigns, amplifying political events, and cyberbullying have become commonplace in the world of social media. To protect our societies, disinformation detection is a critical challenge in combating the spread of false information on social media platforms. Several machine-learning methods have been employed in troll detection to classify and identify troll accounts on social media platforms. On the other hand, there is a lack of research that is aimed to detect and characterize the activities of these accounts. In this paper, an adaptive algorithm is proposed to classify Twitter hashtags if they are normal and clean from Trolls’ interventions or if there are direct amplifying and suspicious activities in them. A new set of relevant features are designed and proposed to be used with the machine learning algorithms. Our experimental results show that the proposed features with Artificial Neural Networks obtain the best results and can reach an accuracy of 91%. We believe that this algorithm can be of great value for governments and decision-makers to not be affected by social media campaigns powered by troll groups and be able to filter these disinformation campaigns easily.
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