Assembly of polarity, emotion and user statistics for detection of fake profiles Notebook for PAN at CLEF 2020

The explosive growth of fake news on social networks has aroused great interest from researchers in different disciplines. To achieve efficient and effective detection of fake news requires scientific contributions from various disciplines, such as computational linguistics, artificial intelligence, and sociology. Here we illustrate how polarity, emotion, and user statistics can be used to detect fake profiles on Twitter’s social network. This paper presents a novel strategy for the characterization of the Twitter profile based on the generation of an assembly of polarity, emotion, and user statistics characteristics that serve as input to a set of classifiers. The results are part of our participation in the PAN 2020 in the CLEF in the task of Profiling Fake News Spreaders on Twitter.