On the Analysis of Users Engaged in Twitter's Trend Topics

Abstract

Online Social Networks (OSN) are virtual environments that allow users to exchange messages, interact, and share content. The amount of information flowing through OSN promotes the competition for attention and influence among users who struggle to co-opt other users to share their message. The influence gained by users can be important to call the attention to target topics so that they eventually become Trend Topics (most popular topics within a time frame). In this work, we illustrate how we can apply concepts of network science to analyze the network structure that represents a Trend Topic. As a consequence, we show how to identify important users that contributed significantly to the topic popularity. In addition, we show how we can detect naive artificial efforts, such as bot activities, to increase the popularity of a user and, consequently, the popularity of the topic.