SocialHelix: visual analysis of sentiment divergence in social media

Abstract

Social media allow people to express and promote different opinions, on which people’s sentiments to a subject often diverge when their opinions conflict. An intuitive visualization that unfolds the process of sentiment divergence from the rich and massive social media data will have far-reaching impact on various domains including social science, politics and economics. In this paper, we propose a visual analysis system, SocialHelix, to achieve this goal. SocialHelix is a novel visual design which enables the users to detect and trace topics and events occurring in social media, and to understand when and why divergences occurred and how they evolved among different social groups. We demonstrate the effectiveness and usefulness of SocialHelix by conducting in-depth case studies on tweets related to the national political debates.