Social media allow people to express and propagate different opinions, in which people's sentiments to a subject often diverge when their opinions conflict. An intuitive visualization that allows for unfolding the process of sentiment divergence from the rich and massive social media data will have far-reaching impact in various domains including social, political and economic. In this poster, we propose a visualization system, SocialHelix, to achieve this goal. SocialHelix is a novel visual design which enables users to detect and trace occurring in social media, and to understand when and why conflicts occurred and how they evolved among different social groups. We demonstrate the effectiveness and usefulness of SocialHelix by conducting in-depth case studies based on Twitter data regarding to the national political debates.