Towards bots detection by analyzing the behavior of user data on Twitter

Abstract

Currently, social networks play an important role as a means of communication about various topics. In this way, this medium represents a very important source of data to know the opinions of its users on very diverse topics. However, the opinions expressed in this medium are exposed to the influence of specialized programs called bots. These bots are activated with the idea of influencing positively or negatively towards some point of view of the issues under discussion. When implemented through computer platforms accessible from any medium with Internet access, it is possible to access such content automatically through its APIs. Prior to an analysis of the opinions expressed in the social network, it would be highly recommended, as part of the process of debugging the data, some reliable bot detection mechanism. While there is still no optimized method for this task, this paper proposes a series of directives that can be considered in order to carry it out. As a case study, these directives are implemented on messages retrieved from Twitter, related to opinions about the candidates of the presidential election of Mexico in 2018.