Bots and gender profiling on twitter using sociolinguistic features notebook for PAN at CLEF 2019

Edwin Puertas, Luis Gabriel Moreno-Sandoval, Flor Miriam Plaza-Del-Arco, Jorge Andres Alvarado-Valencia, Alexandra Pomares-Quimbaya, L. Alfonso Ureña-López

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

Unfortunately, in social networks, software bots or just bots are becoming more and more common because malicious people have seen their usefulness to spread false messages, spread rumors and even manipulate public opinion. Even though the text generated by users in social networks is a rich source of information that can be used to identify different aspects of its authors, not being able to recognize which users are truly humans and which are not, is a big drawback. In this work, we describe the properties of our multilingual classification model submitted for PAN2019 that is able to recognize bots from humans, and females from males. This solution extracted 18 features from the user's posts and applying a machine learning algorithm obtained good performance results.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume2380
StatePublished - 2019
Event20th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2019 - Lugano, Switzerland
Duration: 09 Sep 201912 Sep 2019

Keywords

  • Author profiling
  • Bots profiling
  • Computational linguistic
  • Gender profiling
  • Sociolinguistic
  • User profiling

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