Who You Should Not Follow: Extracting Word Embeddings from Tweets to Identify Groups of Interest and Hijackers in Demonstrations

Recalde L.;Mendieta J.;Boratto L.;Teran L.;Vaca C.;Baquerizo G.

2019-01-01

---

Abstract

In the latest years, a number of citizen movements and protests have spread across the world. One of the characteristics of such events is that demonstrations have been aroused by the use of social networking channels such as Twitter, Facebook, and Whatsapp, among others. Different scholars are currently analyzing this phenomenon to better understand its impact on societies. Furthermore, the use of the Internet as a driver or tool for organizing different groups and demonstrations leaves traces of social changes that have been addressed by technology. Nevertheless, it is important to define ways of identifying different movements, as well as possible misuse by so-called Internet trolls or hijackers, whose objective is to start arguments and confuse or upset other users. In this work, the authors present the case of demonstrations in Ecuador from March 2015 to April 2016 and use data from Twitter users who engaged in those demonstrations. Ecuador has a long history of demonstrations against different governments, which makes this scenario very attractive for more in depth study. Moreover, the authors present a framework for identifying political interest groups as well as possible hashtag hijackers. Specifically, this work focuses on the problem of giving recommendations to groups in which a group of users with the same political view receives suggestions of users they should not follow because they have opposing political views but use hijacked hashtags. Experiments on real-world data collected from the previously mentioned demonstrations show the effectiveness of this approach in automatically identifying hijackers so that they can be effectively recommended to a group as people they should not follow.