Abstract
Social bots are automated programs that generate a significant amount of social media content. This content can be harmful, as it may target a certain audience to influence opinions, often politically motivated, or to promote individuals to appear more popular than they really are. We proposed a set of feature extraction and transformation methods in conjunction with ensemble classifiers for the PAN 2019 Author Profiling task. For the bot identification subtask we used user behaviour fingerprint and statistical diversity measures, while for the gender identification subtask we used a set of text statistics, as well as syntactic information and raw words.
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CITATION STYLE
Kosmajac, D., & Keselj, V. (2020). Twitter User Profiling: Bot and Gender Identification: Notebook for PAN at CLEF 2019. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12260 LNCS, pp. 141–153). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58219-7_13
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