Event Detection for Heterogeneous News Streams

In this paper we tackle the problem of detecting events from multiple and heterogeneous streams of news. In particular, we focus on news which are heterogeneous in length and writing styles since they are published on different platforms (i.e., Twitter, RSS portals, and news websites). This heterogeneity makes the event detection task more challenging, hence we propose an approach able to cope with heterogeneous streams of news. Our technique combines topic modeling, named-entity recognition, and temporal analysis to effectively detect events from news streams. The experimental results confirmed that our approach is able to better detect events than other state-of-the-art techniques and to divide the news in high-precision clusters based on the events they describe.