Searching microblog posts, with their limited length and creative language usage, is challenging. We frame the microblog search problem as a data fusion problem. We examine the effectiveness of a recent cluster-based fusion method on the task of retrieving microblog posts. We find that in the optimal setting the contribution of the clustering information is very limited, which we hypothesize to be due to the limited length of microblog posts. To increase the contribution of the clustering information in cluster-based fusion, we integrate semantic document expansion as a preprocessing step. We enrich the content of microblog posts appearing in the lists to be fused by Wikipedia articles, based on which clusters are created. We verify the effectiveness of our combined document expansion plus fusion method by making comparisons with microblog search algorithms and other fusion methods.

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Authors and Affiliations

1. ISLA, University of Amsterdam, The Netherlands

   Shangsong Liang, Zhaochun Ren & Maarten de Rijke

Editors and Affiliations

1. University of Amsterdam, Amsterdam, The Netherlands

   Maarten de Rijke  & Tom Kenter  & 

2. Centrum Wiskunde en Informatica, Amsterdam, The Netherlands and Delft University of Technology, Delft, The Netherlands

   Arjen P. de Vries

3. University of Illinois at Urbana-Champaign, Urbana, IL, USA

   ChengXiang Zhai

4. University of Twente, Twente, The Netheralnds and Erasmus University Rotterdam, Rotterdam, The Netherlands

   Franciska de Jong

5. SalesPredict, Haifa, Israel

   Kira Radinsky

6. Microsoft Research, Cambridge, UK

   Katja Hofmann

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