{"id":4089,"date":"2021-12-16T11:00:00","date_gmt":"2021-12-16T11:00:00","guid":{"rendered":"https:\/\/pcyti.izt.uam.mx\/?p=4089"},"modified":"2023-01-27T19:44:32","modified_gmt":"2023-01-27T19:44:32","slug":"anomaly-detection-as-a-method-for-uncovering-twitter-bots","status":"publish","type":"post","link":"https:\/\/pcyti.izt.uam.mx\/?p=4089","title":{"rendered":"Seminario: “Anomaly Detection as a Method for Uncovering Twitter Bots”"},"content":{"rendered":"\n

Fecha<\/strong>: jueves 16 de diciembre de 2021 a las 11:00 hrs
Presenta<\/strong>: Dr. Ra\u00fal Monroy Borja
Afiliaci\u00f3n<\/strong>: Escuela de Ingenier\u00eda y Ciencias, Tecnol\u00f3gico de Monterrey, Campus Estado de M\u00e9xico<\/p>\n\n\n\n

Resumen<\/strong>:<\/p>\n\n\n\n

Twitter is a popular online social network with hundreds of millions of users, where an important part of the accounts in this social network are not humans. Approximately 48 million Twitter accounts are managed by automated programs called bots, which represents up to 15% of all accounts. Some bots have good purposes, such as automatically posting information about news and academic papers, and even to provide help during emergencies. Nevertheless, Twitter bots have also been used for malicious purposes, such as distributing malware or influencing the perception of the public about a topic. There are existing mechanisms that allow detecting bots on Twitter automatically; however, these mechanisms rely on examples of existing bots to discern them from legitimate accounts. As the bot landscape changes, with the bot creators using more sophisticated methods to avoid detection, new mechanisms for discerning between legitimate and bot accounts are needed. In this paper, we propose to use one-class classification to enhance Twitter bot detection, as this allows detecting novel bot accounts, and requires only from examples of legitimate accounts. Our experiment results show that our proposal can consistently detect different types of bots with a performance above 0.89 measured using AUC, without requiring previous information about them.<\/p>\n\n\n\n

Semblanza:<\/strong><\/p>\n\n\n\n

Ra\u00fal Monroy obtuvo el grado de doctor, en inteligencia artificial, por la universidad de Edimburgo, en 1998, bajo la supervisi\u00f3n del Prof. Alan Bundy. El Dr. Monroy ha trabajado para el Tecnol\u00f3gico de Monterrey, desde 1985. Es profesor investigador titular; miembro del sistema nacional de investigadores, actualmente nivel 3; miembro regular de la Academia Mexicana de Ciencias; y miembro constituyente de la Academia Mexicana de Computaci\u00f3n. En el Tecnol\u00f3gico de Monterrey, es l\u00edder del grupo de investigaci\u00f3n en modelos de aprendizaje computacional y director de los programas de posgrado en computaci\u00f3n de la regi\u00f3n metropolitana de la ciudad de M\u00e9xico.<\/p>\n\n\n\n

Video del seminario<\/strong>:<\/p>\n\n\n\n

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