Trending topic aggregation by news-based context modeling

Abstract

We tackle social media analysis based on trending topics like “super bowl” and “oscars 2016” acquired from channels such as Twitter or Google. Our approach addresses the identification of semantically related topics (such as “oscars 2016” and “leonardo dicaprio”) by enriching trends with textual context acquired from news search and applying a clustering and tracking in term space. In quantitative experiments on manually annotated trends from Feb–Mar 2016, we demonstrate this approach to work reliably (with an F1-score of > 90%) and to outperform several baselines, including knowledge graph modelling using DBPedia as well as a direct comparison of articles or terms.