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Predicting Hashtag Popularity of Social Emergency by a Robust Feature Extraction Method

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Book cover Knowledge and Systems Sciences (KSS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 780))

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Abstract

Social emergency information is usually disseminated and driven by a hot topic described succinctly with a hashtag in social media. In China, hashtag prediction for social emergencies is more and more practical for E-governance. How to predict the hashtag popularity for social emergency has become a considerably important task. However, previous research mainly focused on commercial hashtag prediction, such as marketing and promotion. For the hashtag popularity prediction, the core issue is to identify the key features for improving prediction accuracy. To the best of our knowledge, there is few research focus on the feature extraction of hashtag for social emergency. In addition, we extract features for hashtag popularity prediction from “seed information” by avoiding excessive crawling. The “seed information” are the microblogs under a hashtag for a 24-h period since the hashtag was published. Based on the “seed information”, the user-based and content-based features are derived, which facilitate the spread of social emergency information. Furthermore, recursive feature elimination (RFE) analysis and nine machine learning classification models are integrated to determine the optimal features among all possible feature combinations. The effectiveness and robustness of our proposed features are verified.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 71403262, 71774154, 71573247, 71503246).

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Authors and Affiliations

  1. Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China

    Qianqian Li

  2. College of Computer Science and Technology, Jilin University, Changchun, China

    Ying Li

Authors
  1. Qianqian Li
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  2. Ying Li
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Corresponding author

Correspondence to Ying Li .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jian Chen

  2. Thammasat University, Bangkadi Muang Pathumthani, Thailand

    Thanaruk Theeramunkong

  3. National Electronics and Computer Technology Center, Pathumthani, Thailand

    Thepachai Supnithi

  4. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Xijin Tang

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Li, Q., Li, Y. (2017). Predicting Hashtag Popularity of Social Emergency by a Robust Feature Extraction Method. In: Chen, J., Theeramunkong, T., Supnithi, T., Tang, X. (eds) Knowledge and Systems Sciences. KSS 2017. Communications in Computer and Information Science, vol 780. Springer, Singapore. https://doi.org/10.1007/978-981-10-6989-5_12

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  • DOI: https://doi.org/10.1007/978-981-10-6989-5_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6988-8

  • Online ISBN: 978-981-10-6989-5

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