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QweetFinder: Real-Time Finding and Filtering of Question Tweets

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Advances in Information Retrieval (ECIR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10193))

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Abstract

Users continuously ask questions and seek answers in social media platforms such as Twitter. In this demo, we present QweetFinder, a Web-based search engine that facilitates finding question tweets (Qweets) in Twitter. QweetFinder listens to Twitter live stream and continuously identifies and indexes tweets that are answer-seeking. QweetFinder also allows users to save queries of long-term interest and pushes real-time qweet matches of saved queries to them via e-mail.

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Notes

  1. 1.

    A prototype of QweetFinder can be found at www.qweetfinder.com.

  2. 2.

    http://www.tweepy.org/.

  3. 3.

    Arabic phrases are available here: http://bit.ly/2itZDe9.

  4. 4.

    Version 3.6.5.

  5. 5.

    https://github.com/flaxsearch/luwak.

  6. 6.

    https://www.playframework.com.

References

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Acknowledgments

This work was made possible by NPRP grant# NPRP 6-1377-1-257 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Author information

Authors and Affiliations

  1. RMIT University, Melbourne, Australia

    Ameer Albahem

  2. Department of Computer Science and Engineering, Qatar University, Doha, Qatar

    Maram Hasanain, Marwan Torki & Tamer Elsayed

Authors
  1. Ameer Albahem
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  2. Maram Hasanain
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  3. Marwan Torki
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  4. Tamer Elsayed
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Corresponding author

Correspondence to Ameer Albahem .

Editor information

Editors and Affiliations

  1. University of Glasgow , Glasgow, United Kingdom

    Joemon M Jose

  2. TU Delft - EWI/ST/WIS , Delft, The Netherlands

    Claudia Hauff

  3. Middle East Technical University , Ankara, Turkey

    Ismail Sengor Altıngovde

  4. Open University , Milton Keynes, United Kingdom

    Dawei Song

  5. Signal Media , London, United Kingdom

    Dyaa Albakour

  6. Toronto, Canada

    Stuart Watt

  7. JohnTait.net Ltd. and BCS IRSG , Sunderland, United Kingdom

    John Tait

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Albahem, A., Hasanain, M., Torki, M., Elsayed, T. (2017). QweetFinder: Real-Time Finding and Filtering of Question Tweets. In: Jose, J., et al. Advances in Information Retrieval. ECIR 2017. Lecture Notes in Computer Science(), vol 10193. Springer, Cham. https://doi.org/10.1007/978-3-319-56608-5_79

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  • DOI: https://doi.org/10.1007/978-3-319-56608-5_79

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56607-8

  • Online ISBN: 978-3-319-56608-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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