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Do Rumors Diffuse Differently from Non-rumors? A Systematically Empirical Analysis in Sina Weibo for Rumor Identification

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Advances in Knowledge Discovery and Data Mining (PAKDD 2017)

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Abstract

With the prosperity of social media, online rumors become a severe social problem, which often lead to serious consequences, e.g., social panic and even chaos. Therefore, how to automatically identify rumors in social media has attracted much research attention. Most existing studies address this problem by extracting features from the contents of rumors and their reposts as well as the users involved. For these features, especially diffusion features, these works ignore systematic analysis and the exploration of difference between rumors and non-rumors, which exert targeted effect on rumor identification. In this paper, we systematically investigate this problem from a diffusion perspective using Sina Weibo data. We first extract a group of new features from the diffusion processes of messages and then make a few important observations on them. Based on these features, we develop classifiers to discriminate rumors and non-rumors. Experimental comparisons with the state-of-the-arts methods demonstrate the effectiveness of these features.

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Acknowledgments

This work was funded by the National Basic Research Program of China (973 Program) under Grant Number 2013CB329602, the National Key Research and Development Program of China under Grant Number 2016YFB1000902, and the National Natural Science Foundation of China under Grant Numbers 61572473, 61472400, 61232010. H.W. Shen is also funded by Youth Innovation Promotion Association CAS and the CCF-Tencent RAGR (No. 20160107).

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Authors and Affiliations

  1. CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Yahui Liu, Xiaolong Jin, Huawei Shen & Xueqi Cheng

Authors
  1. Yahui Liu
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  2. Xiaolong Jin
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  3. Huawei Shen
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  4. Xueqi Cheng
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Corresponding author

Correspondence to Yahui Liu .

Editor information

Editors and Affiliations

  1. Kangwon National University, Chuncheon, Korea (Republic of)

    Jinho Kim

  2. Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. University of Technology Sydney, Sydney, New South Wales, Australia

    Longbing Cao

  4. KAIST, Daejeon, Korea (Republic of)

    Jae-Gil Lee

  5. University of New South Wales, Sydney, New South Wales, Australia

    Xuemin Lin

  6. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Liu, Y., Jin, X., Shen, H., Cheng, X. (2017). Do Rumors Diffuse Differently from Non-rumors? A Systematically Empirical Analysis in Sina Weibo for Rumor Identification. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_32

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  • DOI: https://doi.org/10.1007/978-3-319-57454-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57453-0

  • Online ISBN: 978-3-319-57454-7

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