This paper examines the effect of online social network interactions on future attitudes. Specifically, we focus on how a person's online content and network dynamics can be used to predict future attitudes and stances in the aftermath of a major event. In this study, we focus on the attitudes of US Twitter users towards Islam and Muslims subsequent to the tragic Paris terrorist attacks that occurred on November 13, 2015. We quantitatively analyze 44K users' network interactions and historical tweets to predict their attitudes. We provide a description of the quantitative results based on the content (hashtags) and network interaction (retweets, replies, and mentions). We analyze two types of data: (1) we use post-event tweets to learn users' stated stances towards Muslims based on sampling methods and crowd-sourced annotations; and (2) we employ pre-event interactions on Twitter to build a classifier to predict post-event stances. We found that pre-event network interactions can predict someone's attitudes towards Muslims with 82% macro F-measure, even in the absence of prior mentions of Islam, Muslims, or related terms.
As an example, Magdy et al. REF attempted to predict the attitudes of US Twitter users towards Islam and Muslims subsequent to the tragic Paris terrorist attacks that occurred on November 13, 2015.
#ISISisNotIslam or #DeportAllMuslims?: predicting unspoken views
WebSci '16
Sociology Computer Science