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

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. © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

• Fecha de publicación: 2019
• Institución: Universidad Tecnológica de Bolívar
• Repositorio: Repositorio Institucional UTB
• Idioma: eng
• OAI Identifier: oai:repositorio.utb.edu.co:20.500.12585/9191
• Acceso en línea: https://hdl.handle.net/20.500.12585/9191
• Palabra clave: Author profiling; Bots profiling; Computational linguistic; Gender profiling; Sociolinguistic; User profiling; Character recognition; Classification (of information); Computational linguistics; Learning algorithms; Linguistics; Machine learning; Social aspects; Social networking (online); Social sciences computing; Author profiling; Bots profiling; Gender profiling; Sociolinguistic; User profiling; Botnet
• Rights: restrictedAccess
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/