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Social Events Forecasting in Microblogging

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Brain Informatics (BI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10654))

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Abstract

Along with the popularization and rapid development of Internet, there is a growing interest in the research to identify the trend of social events on social media. Currently news could quickly spread on various social media (e.g. Sina Weibo) with a limited time, which may trigger the severity of the events that requires timely attention and responses from government. This paper proposes to predict the trend of social events on Sina Weibo, which is the most popular social media in China now. In this study, combining social psychology and communication sciences, we extracted comprehensive and effective features which may relate to the trend of social events on social media, and constructed the trend prediction models using three classical regression algorithms. The real social events data was used to verify the performance of our model, and the outstanding performance with precision of 0.56 and an f-measure of 0.71 demonstrate the efficiency of our features and models.

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Acknowledgements

The authors gratefully acknowledges the generous support from Natural Science Foundation of Hubei Province (2016CFB208).

Author information

Authors and Affiliations

  1. Institute of Psychology, Chinese Academy of Sciences, Beijing, China

    Yang Zhou, Xiaoqian Liu, Jingying Wang & Tingshao Zhu

  2. University of Chinese Academy of Sciences, Beijing, China

    Yang Zhou, Jingying Wang & Yuanbo Gao

  3. Beihang University, Beijing, China

    Chuxue Zhang

  4. School of Information Engineering, Hubei University of Economics, Wuhan, Hubei, China

    Shuotian Bai

Authors
  1. Yang Zhou
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  2. Chuxue Zhang
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  3. Xiaoqian Liu
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  4. Jingying Wang
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  5. Yuanbo Gao
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  6. Shuotian Bai
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  7. Tingshao Zhu
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Corresponding author

Correspondence to Tingshao Zhu .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Yi Zeng

  2. Beijing Normal University, Beijing, China

    Yong He

  3. KTH Royal Institute of Technology and Karolinska Institute, Stockholm, Sweden

    Jeanette Hellgren Kotaleski

  4. University of California, San Diego, San Diego, California, USA

    Maryann Martone

  5. Chinese Academy of Sciences, Beijing, China

    Bo Xu

  6. Allen Institute for Brain Science, Seattle, Washington, USA

    Hanchuan Peng

  7. Wuhan National Lab Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

    Qingming Luo

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Zhou, Y. et al. (2017). Social Events Forecasting in Microblogging. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_22

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  • DOI: https://doi.org/10.1007/978-3-319-70772-3_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70771-6

  • Online ISBN: 978-3-319-70772-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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