Title: Semantic Annotation for Microblog Topics Using Wikipedia Temporal Information,
Abstract: Trending topics in microblogs such as Twitter are valuable resources to
understand social aspects of real-world events. To enable deep analyses of such
trends, semantic annotation is an effective approach; yet the problem of
annotating microblog trending topics is largely unexplored by the research
community. In this work, we tackle the problem of mapping trending Twitter
topics to entities from Wikipedia. We propose a novel model that complements
traditional text-based approaches by rewarding entities that exhibit a high
temporal correlation with topics during their burst time period. By exploiting
temporal information from the Wikipedia edit history and page view logs, we
have improved the annotation performance by 17-28\%, as compared to the
competitive baselines.