03 A Multi-agent Approach for Online Twitter Bot Detection
Online social networks are tools that allow interaction between human beings with a large number of users.Platforms like Twitter present the problem of social bots which are controlled by automated agents potentially used for malicious activities. Thus, social bot detection is important to keep peop...
| Autores: | , , |
|---|---|
| Formato: | capítulo de livro |
| Fecha de publicación: | 2021 |
| País: | España |
| Recursos: | Universidad de Castilla-La Mancha |
| Repositorio: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/28601 |
| Acesso em linha: | http://doi.org/10.18239/jornadas_2021.34.03 http://hdl.handle.net/10578/28601 |
| Access Level: | acceso abierto |
| Palavra-chave: | Bot detection Social bots Multi-Agent System MAS |
| Resumo: | Online social networks are tools that allow interaction between human beings with a large number of users.Platforms like Twitter present the problem of social bots which are controlled by automated agents potentially used for malicious activities. Thus, social bot detection is important to keep people safe from harmful effects. In this work, we approach the Twitter bot online detection problem with a multi-agent system (MAS). It is based on supervised classification with three machine learning algorithms and a reduced set of features. The MAS performance compared to the three algorithms applied separately - Random Forest, Support Vector Machine, and Na¨ıve Bayes - presented similar results. Besides, interesting results for online bot detection with the MAS prototype suggested that 88.19% of bots detected were correctly labeled. The results indicate that the approach used is feasible and promising for the real-time bot detection problem. |
|---|