Effects of emotion and topic area on topic shifts in social media discussions

Nowadays, discussing and commenting interactively on an article in Internet-based social media platforms is pervasive. The topic of a comment/reply in these discussions occasionally shifts, sometimes drastically and abruptly, other times slightly, away from the topic of the article. In this paper, we model and study the topic shift phenomena in article-originated social media comments, and identify quantitatively the effects on topic shifts of comments’ (1) emotion levels (of various emotion dimensions), (2) topic areas, and (3) the structure of the discussion tree. We then propose and evaluate a new approach to measure and visualize named emotion scores of comment sets. We show that, with a better understanding of the topic shift phenomena in comments, personalized automated systems can be built to cater to comment-browsing and comment-viewing needs of different users.