Microblogging platforms such as Twitter provide low cost access to an immense reserve of authoritative professionals, opinion leaders and hobbyists for a wide range of topics. Yet, as microposts are short and incredibly diverse, many of these experts are hidden. In this paper, we present e#, a system to retrieve experts automatically for a given set of keywords. Our design targets exhaustivity: e# can detect previously undetectable experts. The core idea is to enhance a state-ofthe-art expert detection algorithm with a graph of expertise domains. Our system produces this graph from hundreds of Gigabytes of Web search query logs and behavioral data, processed in a distributed, parallel fashion. We provide a detailed description of our architecture, including an original SQL-based community detection algorithm. We then benchmark our system with 750 queries, using crowdsourcing. We observe that e# finds many more experts than a state-of-the-art baseline.

Additional Metadata
Keywords Clustering, Expert detection, Query expansion
Stakeholder Microsoft , Snowflake
Persistent URL dx.doi.org/10.5441/002/edbt.2016.53
Conference International Conference on Extending Database Technology
Sellam, T.H.J, Hentschel, M, Alonso, O, & Kandylas, V. (2016). E#: Sharper expertise detection from microblogs. In Proceedings of the International Conference on Extending Database Technology (pp. 563–572). doi:10.5441/002/edbt.2016.53