research-article

Predicting the social influence of upcoming contents in large social networks

Authors:

Yan JiaAuthors Info & Claims

ICIMCS '13: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service

Pages 17 - 22

https://doi.org/10.1145/2499788.2499834

Published: 17 August 2013 Publication History

Abstract

Online social networks, such as twitter and facebook, are continuously generating the new contents and relationships. To fully understand the spread of topics, there are some essential but remaining open questions. Why do some seemingly ordinary topics actually received widespread attention? Is it due to the attractiveness of the content itself, or social network structure plays a larger role in the dissemination of information? Can we predict the trend of information dissemination?

Analyzing and predicting the influence and spread of up-coming contents is an interesting and useful research direction, and has brilliant perspective on web advertising and spam detection. For solving the problems, in this paper, a novel time series model has been proposed. In this model, the existing user-generated contents are summarized with a set of valued sequences. An early predictor is adopted for analyzing the topical/structural properties of series, and the influence of newly coming contents are estimated with the predictor. The empirical study conducted on large real data sets indicates that our model is interesting and meaningful, and our methods are effective and efficient in practice.

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Cited By

Hai-Jew S(2022)Self-Presenting Virtually for Remote Social InfluencePractical Peer-to-Peer Teaching and Learning on the Social Web10.4018/978-1-7998-6496-7.ch013(407-461)Online publication date: 2022
https://doi.org/10.4018/978-1-7998-6496-7.ch013
Miao ZChen KFang YHe JZhou YZhang WZha H(2016)Cost-Effective Online Trending Topic Detection and Popularity Prediction in MicrobloggingACM Transactions on Information Systems (TOIS)10.1145/300183335:3(1-36)Online publication date: 15-Dec-2016
https://dl.acm.org/doi/10.1145/3001833

Index Terms

Predicting the social influence of upcoming contents in large social networks
1. Information systems
  1. Information retrieval
  2. Information storage systems

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Published In

cover image ACM Other conferences

ICIMCS '13: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service

August 2013

419 pages

ISBN:9781450322522

DOI:10.1145/2499788

Conference Chair:
Tat-Seng Chua
National University of Singapore, Singapore
,
General Chairs:
Ke Lu
University of Chinese Academy of Sciences, China
,
Tao Mei
Microsoft Research Asia, China
,
Xindong Wu
Hefei University of Technology, China

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

NSF of China: National Natural Science Foundation of China
University of Sciences & Technology, Hefei: University of Sciences & Technology, Hefei
Beijing ACM SIGMM Chapter

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 August 2013

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Funding Sources

National Key Technology R&D Program of China
National Natural Science Foundation of China
Ministry of Science and Technology of the People's Republic of China
China Postdoctoral Science Foundation

Conference

ICIMCS '13

Sponsor:

NSF of China
University of Sciences & Technology, Hefei

ICIMCS '13: International Conference on Internet Multimedia Computing and Service

August 17 - 19, 2013

Huangshan, China

Acceptance Rates

ICIMCS '13 Paper Acceptance Rate 20 of 94 submissions, 21%;

Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

Hai-Jew S(2022)Self-Presenting Virtually for Remote Social InfluencePractical Peer-to-Peer Teaching and Learning on the Social Web10.4018/978-1-7998-6496-7.ch013(407-461)Online publication date: 2022
https://doi.org/10.4018/978-1-7998-6496-7.ch013
Miao ZChen KFang YHe JZhou YZhang WZha H(2016)Cost-Effective Online Trending Topic Detection and Popularity Prediction in MicrobloggingACM Transactions on Information Systems (TOIS)10.1145/300183335:3(1-36)Online publication date: 15-Dec-2016
https://dl.acm.org/doi/10.1145/3001833

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