Improving Twitter Retrieval by Exploiting Structural Information

Most Twitter search systems generally treat a tweet as a plain text when modeling relevance. However, a series of conventions allows users to tweet in structural ways using combination of different blocks of texts. These blocks include plain texts, hashtags, links, mentions, etc. Each block encodes a variety of communicative intent and sequence of these blocks captures changing discourse. Previous work shows that exploiting the structural information can improve the structured document (e. g. , web pages) retrieval. In this paper we utilize the structure of tweets, induced by these blocks, for Twitter retrieval. A set of features, derived from the blocks of text and their combinations, is used into a learning-to-rank scenario. We show that structuring tweets can achieve state-of-the-art performance. Our approach does not rely upon social media features, but when we do add this additional information, performance improves significantly.

Authors

• Zhunchen Luo National University of Defense Technology
• Miles Osborne The University of Edinburgh
• Saša Petrovic ́ The University of Edinburgh
• Ting Wang City University of Hong Kong, Hong Kong SAR, China

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