Approaches to the Profiling Fake News Spreaders on Twitter Task in English and Spanish.

. This paper discusses the decisions made approaching PANs Profiling Fake News Spreaders on Twitter Task at CLEF 2020. We briefly describe how we combined author tweets to create samples that do or do not represent a Fake News Spreader. We decided to handle both languages proposed for this task: Spanish and English; and the methodologies that we suggested were Linear Support Vector Machines (SVMs) and Gradient Boosting, respectively. Other approaches such as Long Short-Term Memory (LSTM) were taken into account in the process of finding a model with the best accuracy results and these were also reported in this paper. We made use of the cross-validation scenario to obtain accuracy results due to the reduced amount of data. We have managed to achieve average accuracy scores of 0.735 for the Spanish language identification task and 0.685 for the English language identification task.