Improving Twitter Retrieval by Exploiting Structural Information

Authors

  • Zhunchen Luo National University of Defense Technology
  • Miles Osborne The University of Edinburgh
  • Saša Petrovic ́ The University of Edinburgh
  • Ting Wang National University of Defense Technology

DOI:

https://doi.org/10.1609/aaai.v26i1.8198

Abstract

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.

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Published

2021-09-20

How to Cite

Luo, Z., Osborne, M., Petrovic ́ S., & Wang, T. (2021). Improving Twitter Retrieval by Exploiting Structural Information. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 648-654. https://doi.org/10.1609/aaai.v26i1.8198