Information diffusion pattern mining over online social media

Hsueh-Chan Lu, Hui Ju Hung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationTrends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops
Subtitle of host publicationDANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers
EditorsWen-Chih Peng, Haixun Wang, Zhi-Hua Zhou, Tu Bao Ho, Vincent S. Tseng, Arbee L.P. Chen, James Bailey
PublisherSpringer Verlag
Pages137-148
Number of pages12
ISBN (Electronic)9783319131856
DOIs
Publication statusPublished - 2014 Jan 1
EventInternational Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan
Duration: 2014 May 132014 May 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8643
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014
CountryTaiwan
CityTainan
Period14-05-1314-05-16

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lu, H-C., & Hung, H. J. (2014). Information diffusion pattern mining over online social media. In W-C. Peng, H. Wang, Z-H. Zhou, T. B. Ho, V. S. Tseng, A. L. P. Chen, & J. Bailey (Eds.), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers (pp. 137-148). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8643). Springer Verlag. https://doi.org/10.1007/978-3-319-13186-3_13