Social media bots pose as humans to influence users with commercial, political or ideological purposes. For example, bots could artificially inflate the popularity of a product by promoting it and/or writing positive ratings, as well as undermine the reputation of competitive products through negative valuations. The threat is even greater when the purpose is political or ideological (see Brexit referendum or US Presidential elections). Fearing the effect of this influence, the German political parties have rejected the use of bots in their electoral campaign for the general elections. Furthermore, bots are commonly related to fake news spreading. Therefore, to approach the identification of bots from an author profiling perspective is of high importance from the point of view of marketing, forensics and security. After having addressed several aspects of author profiling in social media from 2013 to 2018 (age and gender, also together with personality, gender and language variety, and gender from a multimodality perspective), this year we aim at investigating whether the author of a Twitter feed is a bot or a human. Furthermore, in case of human, to profile the gender of the author. The uncompressed dataset consists in a folder per language (en, es). Each folder contains: A XML file per author (Twitter user) with 100 tweets. The name of the XML file correspond to the unique author id. A truth.txt file with the list of authors and the ground truth.

{"references": ["Francisco Rangel and Paolo Rosso. Overview of the 7th Author Profiling Task at PAN 2019: Bots and Gender Profiling. In Linda Cappellato, Nicola Ferro, David E. Losada, and Henning M\u00fcller, editors, CLEF 2019 Labs and Workshops, Notebook Papers, September 2019. CEUR-WS.org."]}