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Hashtag-based topic evolution in social media

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Abstract

The rise of online social media has led to an explosion of metadata-containing user generated content. The tracking of metadata distribution is essential to understand social media. This paper presents two statistical models that detect interpretable topics over time along with their hashtags distribution. A topic is represented by a cluster of words that frequently occur together, and a context is represented by a cluster of hashtags, i.e., the hashtag distribution. The models combine a context with a related topic by jointly modeling words with hashtags and time. Experiments with real-world datasets demonstrate that the proposed models discover topics over time with related contexts effectively.

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Notes

  1. http://www.google.com/trends/

  2. https://twitter.com/

  3. https://foursquare.com/

  4. http://en.wikipedia.org/wiki/Sinking_of_the_MV_Sewol

  5. http://en.wikipedia.org/wiki/Nelson_Mandela

  6. http://hunspell.sourceforge.net/

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Acknowledgment

This research was supported by the Basic Science Research Program and the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (numbers 2015R1A2A1A10052665, 2015R1A2A1A15052701 and 2012M3C4A7033344).

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  1. Department of Computer Science, Korea University, Seoul, Republic of Korea

    Md. Hijbul Alam, Woo-Jong Ryu & SangKeun Lee

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  1. Md. Hijbul Alam
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  2. Woo-Jong Ryu
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  3. SangKeun Lee
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Correspondence to SangKeun Lee.

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Alam, M.H., Ryu, WJ. & Lee, S. Hashtag-based topic evolution in social media. World Wide Web 20, 1527–1549 (2017). https://doi.org/10.1007/s11280-017-0451-3

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