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Hot topic prediction considering influence and expertise in social media

Author

Listed:
  • Kyoungsoo Bok

    (Chungbuk National University)

  • Yeonwoo Noh

    (Chungbuk National University)

  • Jongtae Lim

    (Chungbuk National University)

  • Jaesoo Yoo

    (Chungbuk National University)

Abstract

The hot topic detection designed to identify the recent issues and trends employs the analysis of real-time social media activities. The existing schemes suffer from low precision because they focus on keyword occurrence frequency in documents written by the unspecified majority. The existing schemes are incapable of predicting near-future hot topics as they are intended to detect hot topics at a particular time. We propose a new hot topic prediction scheme considering users’ influence and expertise in social media. The proposed scheme detects expected near-future hot topics by extracting a set of candidate keywords from social-media posts using the modified TF-IDF. The hot topic prediction index is calculated for each candidate keyword based on the influence and expertise of users who include it in their posts and hot topic predictions are performed based on the change rate over time. Finally, a comparison between existing and proposed hot topic detection schemes demonstrates the proposed scheme’s superiority.

Suggested Citation

  • Kyoungsoo Bok & Yeonwoo Noh & Jongtae Lim & Jaesoo Yoo, 2021. "Hot topic prediction considering influence and expertise in social media," Electronic Commerce Research, Springer, vol. 21(3), pages 671-687, September.
    • Handle: RePEc:spr:elcore:v:21:y:2021:i:3:d:10.1007_s10660-018-09327-2
    DOI: 10.1007/s10660-018-09327-2
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    References listed on IDEAS

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    1. Safique Ahmed Faruque & Mossa. Anisa Khatun & Md. Saidur Rahman, 2016. "Modelling direct marketing campaign on social networks," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 22(4), pages 422-435.
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    3. Giancarlo Sperlì & Flora Amato & Fabio Mercorio & Mario Mezzanzanica & Vincenzo Moscato & Antonio Picariello, 2018. "A Social Media Recommender System," International Journal of Multimedia Data Engineering and Management (IJMDEM), IGI Global, vol. 9(1), pages 36-50, January.
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