Hotspots of news articles: Joint mining of news text & social media to discover controversial points in news

We propose and study a novel problem of mining news text and social media jointly to discover controversial points in news, which enables many applications such as highlighting controversial points in news articles for readers, revealing controversies in news and their trends over time, and quantifying the controversy of a news source. We design a controversy scoring function to discover the most controversial sentences in a news article by leveraging relevant comments in Twitter and comments on news web sites to assess the controversy of opinions about an issue mentioned in the news article. Multiple scoring strategies based on sentiment analysis and linguistic cues are proposed and studied. Experimental results show that the proposed algorithms can effectively discover controversial parts in news articles.