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EEST: Entity-Driven Exploratory Search for Twitter

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Information Retrieval Technology (AIRS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9460))

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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.

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Notes

  1. 1.

    https://github.com/lintool/twitter-tools/wiki/TREC-2014-Track-Guidelines.

  2. 2.

    https://code.google.com/p/word2vec/.

  3. 3.

    https://dev.twitter.com/rest/public/search.

References

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Acknowledgment

The work reported in this paper was supported by the National Natural Science Foundation of China Grant 61370116.

Author information

Authors and Affiliations

  1. Institute of Computer Science and Technology, Peking University, Beijing, 100871, China

    Chao Lv, Runwei Qiang, Lili Yao & Jianwu Yang

Authors
  1. Chao Lv
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  2. Runwei Qiang
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  3. Lili Yao
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  4. Jianwu Yang
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Corresponding author

Correspondence to Jianwu Yang .

Editor information

Editors and Affiliations

  1. Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia

    Guido Zuccon

  2. Brisbane, Queensland, Australia

    Shlomo Geva

  3. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  4. RMIT University, Melbourne, Australia

    Falk Scholer

  5. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Aixin Sun

  6. Tianjin University, China

    Peng Zhang

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© 2015 Springer International Publishing Switzerland

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Lv, C., Qiang, R., Yao, L., Yang, J. (2015). EEST: Entity-Driven Exploratory Search for Twitter. In: Zuccon, G., Geva, S., Joho, H., Scholer, F., Sun, A., Zhang, P. (eds) Information Retrieval Technology. AIRS 2015. Lecture Notes in Computer Science(), vol 9460. Springer, Cham. https://doi.org/10.1007/978-3-319-28940-3_38

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  • DOI: https://doi.org/10.1007/978-3-319-28940-3_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28939-7

  • Online ISBN: 978-3-319-28940-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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