SocialHelix: Visual Analysis of Sentiment Divergence inSocial Media

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