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Hot topic prediction considering influence and expertise in social media

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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.

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Acknowledgements

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. 2016R1A2B3007527), by “Human Resources Program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 20164030201330), and by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (No. NRF-2017M3C4A7069432).

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Authors and Affiliations

  1. Department of Information and Communication Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk, 28644, Korea

    Kyoungsoo Bok, Yeonwoo Noh, Jongtae Lim & Jaesoo Yoo

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  1. Kyoungsoo Bok
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  2. Yeonwoo Noh
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Correspondence to Jaesoo Yoo.

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Bok, K., Noh, Y., Lim, J. et al. Hot topic prediction considering influence and expertise in social media. Electron Commer Res 21, 671–687 (2021). https://doi.org/10.1007/s10660-018-09327-2

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