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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2014: Trends and Applications in Knowledge Discovery and Data Mining pp 137–148Cite as

Information Diffusion Pattern Mining over Online Social Media

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8643))

Abstract

With the rapid development of Web 2.0 technology, online social media have become increasingly popular and influential. In online social media, such as Reddit, Digg, Twitter and Weibo, users can post, vote and comment posted stories and other users’ comments. Users, together with story and corresponding feedbacks, form a heterogeneous information diffusion network. To analyze how information diffuses among different users, we need to better understand a few key factors, including (1) frequent appearing sub-structures, called motifs, in the network and (2) evolution of a motif. In this paper, we explore the MOtif-based Sequential Pattern (MOSP) to facilitate the understanding of motif evolution along the time. Furthermore, we propose Topological MOSP (T-MOSP) and Propagative MOSP (P-MOSP) to observe frequent sequence of motifs in different angles. Facing a large volume of graph data, Motif mining is time-consuming. Therefore, we devise efficient mining algorithms, namely, Motif-Mine and Lattice-based Temporal Sequential Pattern Mine (LTSP-Mine), to discover motifs and sequences of motifs, respectively. Extensive experimental evaluation on Digg demonstrates that T-MOSP and P-MOSP discovered by the proposed algorithms can efficiently and effectively capture and summarize the information diffusion patterns in online social media.

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Author information

Authors and Affiliations

  1. Department of CSIE, National Taitung University, Taitung, Taiwan

    Eric Hsueh-Chan Lu

  2. Academia Sinica, Taipei, Taiwan

    Hui-Ju Hung

Authors
  1. Eric Hsueh-Chan Lu
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  2. Hui-Ju Hung
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Corresponding author

Correspondence to Eric Hsueh-Chan Lu .

Editor information

Editors and Affiliations

  1. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  2. Google Research, Mountain View, California, USA

    Haixun Wang

  3. University of Melbourne, Melbourne, Victoria, Australia

    James Bailey

  4. National Cheng Kung University, Tainan, Taiwan

    Vincent S. Tseng

  5. Japan Advanced Institute of Science and Technology, Nomi City, Japan

    Tu Bao Ho

  6. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  7. National Chengchi University, Taipei, Taiwan

    Arbee L.P. Chen

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© 2014 Springer International Publishing Switzerland

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Lu, E.HC., Hung, HJ. (2014). Information Diffusion Pattern Mining over Online Social Media. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_13

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  • DOI: https://doi.org/10.1007/978-3-319-13186-3_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13185-6

  • Online ISBN: 978-3-319-13186-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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