Detection of conspiracy propagators using psycho-linguistic characteristics

The rise of social media has offered a fast and easy way for the propagation of conspiracy theories and other types of disinformation. Despite the research attention that has received, fake news detection remains an open problem and users keep sharing articles that contain false statements but which they consider real. In this article, we focus on the role of users in the propagation of conspiracy theories that is a specific type of disinformation. First, we compare profile and psycho-linguistic patterns of online users that tend to propagate posts that support conspiracy theories and of those who propagate posts that refute them. To this end, we perform a comparative analysis over various profile, psychological and linguistic characteristics using social media texts of users that share posts about conspiracy theories. Then, we compare the effectiveness of those characteristics for predicting whether a user is a conspiracy propagator or not. In addition, we propose ConspiDetector, a model that is based on a convolutional neural network (CNN) and which combines word embeddings with psycho-linguistic characteristics extracted from the tweets of users to detect conspiracy propagators. The results show that ConspiDetector can improve the performance in detecting conspiracy propagators by 8.82% compared with the CNN baseline with regard to F1-metric.