EEST: Entity-Driven Exploratory Search for Twitter

Abstract

Social media has become a comprehensive platform for users to obtain information. When searching over the social media, users’ search intents are usually related to one or more entities. Entity, which usually conveys rich information for modeling relevance, is a common choice for query expansion. Previous works usually focus on entities from single source, which are not adequate to cover users’ various search intents. Thus, we propose EEST, a novel multi-source entity-driven exploratory search engine to help users quickly target their real information need. EEST extracts related entities and corresponding relationship information from multi-source (i.e., Google, Twitter and Freebase) in the first phase. These entities are able to help users better understand hot aspects of the given query. Expanded queries will be generated automatically while users choose one entity for further exploration. In the second phase, related users and representative tweets are offered to users directly for quickly browsing. A demo of EEST is available at http://demo.webkdd.org.