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Extracting social events for learning better information diffusion models

Authors:

Philip S. YuAuthors Info & Claims

KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 365 - 373

https://doi.org/10.1145/2487575.2487584

Published: 11 August 2013 Publication History

Abstract

Learning of the information diffusion model is a fundamental problem in the study of information diffusion in social networks. Existing approaches learn the diffusion models from events in social networks. However, events in social networks may have different underlying reasons. Some of them may be caused by the social influence inside the network, while others may reflect external trends in the ``real world''. Most existing work on the learning of diffusion models does not distinguish the events caused by the social influence from those caused by external trends.

In this paper, we extract social events from data streams in social networks, and then use the extracted social events to improve the learning of information diffusion models. We propose a LADP (Latent Action Diffusion Path) model to incorporate the information diffusion model with the model of external trends, and then design an EM-based algorithm to infer the diffusion probabilities, the external trends and the sources of events efficiently.

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Cited By

Hung CGao XLiu ZChai YLiu TLiu C(2023)CECM: A cognitive emotional contagion model in social networksMultimedia Tools and Applications10.1007/s11042-023-15394-x83:1(1001-1023)Online publication date: 30-May-2023
https://doi.org/10.1007/s11042-023-15394-x
Wang JBan Z(2020)Sampling Topic Representative Users by Integrating Node Degree and Edge Weight2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)10.1109/DSC50466.2020.00062(356-361)Online publication date: Jul-2020
https://doi.org/10.1109/DSC50466.2020.00062
Chang HShen H(2019)A Modified Community-Level Diffusion Extraction in Social Network2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)10.1109/PDCAT46702.2019.00101(509-512)Online publication date: Dec-2019
https://doi.org/10.1109/PDCAT46702.2019.00101
Show More Cited By

Index Terms

Extracting social events for learning better information diffusion models
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '13: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2013

1534 pages

ISBN:9781450321747

DOI:10.1145/2487575

Editors:
Rayid Ghani
University of Chicago
,
Ted E. Senator
SAIC
,
Paul Bradley
MethodCare, Inc.
,
Rajesh Parekh
Groupon
,
Jingrui He
Stevens Institute of Technology
,
General Chairs:
Robert L. Grossman
University of Chicago and Open Data Group
,
Ramasamy Uthurusamy
General Motors Corporation (retired)
,
Program Chairs:
Inderjit S. Dhillon
University of Texas
,
Yehuda Koren
Google

Copyright © 2013 ACM.

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Publication History

Published: 11 August 2013

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KDD' 13

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KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 11 - 14, 2013

Illinois, Chicago, USA

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KDD '13 Paper Acceptance Rate 125 of 726 submissions, 17%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Hung CGao XLiu ZChai YLiu TLiu C(2023)CECM: A cognitive emotional contagion model in social networksMultimedia Tools and Applications10.1007/s11042-023-15394-x83:1(1001-1023)Online publication date: 30-May-2023
https://doi.org/10.1007/s11042-023-15394-x
Wang JBan Z(2020)Sampling Topic Representative Users by Integrating Node Degree and Edge Weight2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC)10.1109/DSC50466.2020.00062(356-361)Online publication date: Jul-2020
https://doi.org/10.1109/DSC50466.2020.00062
Chang HShen H(2019)A Modified Community-Level Diffusion Extraction in Social Network2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)10.1109/PDCAT46702.2019.00101(509-512)Online publication date: Dec-2019
https://doi.org/10.1109/PDCAT46702.2019.00101
Meng QLuo JLiu BSun XCao J(2019)Quantifying Group Influence on Individuals in Online Social Networks2019 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC47284.2019.8969773(1-6)Online publication date: Jun-2019
https://doi.org/10.1109/ISCC47284.2019.8969773
Su SWang YZhang ZChang CZia M(2018)Identifying and tracking topic-level influencers in the microblog streamsMachine Language10.1007/s10994-017-5665-1107:3(551-578)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s10994-017-5665-1
Gao TBao WLi JGao XKong BTang YChen GLi X(2018)DancingLines: An Analytical Scheme to Depict Cross-Platform Event PopularityDatabase and Expert Systems Applications10.1007/978-3-319-98809-2_18(283-299)Online publication date: 9-Aug-2018
https://doi.org/10.1007/978-3-319-98809-2_18
Cai HZheng VZhu FChang KHuang Z(2017)From community detection to community profilingProceedings of the VLDB Endowment10.14778/3067421.306743010:7(817-828)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.14778/3067421.3067430
Zhan QZhang JYu PEmery SXie J(2017)Inferring Social Influence of Anti-Tobacco Mass Media CampaignIEEE Transactions on NanoBioscience10.1109/TNB.2017.270707516:5(356-366)Online publication date: Jul-2017
https://doi.org/10.1109/TNB.2017.2707075
Hua TNing YChen FLu CRamakrishnan N(2016)Topical analysis of interactions between news and social mediaProceedings of the Thirtieth AAAI Conference on Artificial Intelligence10.5555/3016100.3016317(2964-2971)Online publication date: 12-Feb-2016
https://dl.acm.org/doi/10.5555/3016100.3016317
Zhang JYu PLv YZhan QMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Information Diffusion at WorkplaceProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983848(1673-1682)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983848
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