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research-article

Discovering multirelational structure in social media streams

Published: 03 February 2012 Publication History

Abstract

In this article, we present a novel algorithm to discover multirelational structures from social media streams. A media item such as a photograph exists as part of a meaningful interrelationship among several attributes, including time, visual content, users, and actions. Discovery of such relational structures enables us to understand the semantics of human activity and has applications in content organization, recommendation algorithms, and exploratory social network analysis.
We are proposing a novel nonnegative matrix factorization framework to characterize relational structures of group photo streams. The factorization incorporates image content features and contextual information. The idea is to consider a cluster as having similar relational patterns; each cluster consists of photos relating to similar content or context. Relations represent different aspects of the photo stream data, including visual content, associated tags, photo owners, and post times. The extracted structures minimize the mutual information of the predicted joint distribution. We also introduce a relational modularity function to determine the structure cost penalty, and hence determine the number of clusters. Extensive experiments on a large Flickr dataset suggest that our approach is able to extract meaningful relational patterns from group photo streams. We evaluate the utility of the discovered structures through a tag prediction task and through a user study. Our results show that our method based on relational structures, outperforms baseline methods, including feature and tag frequency based techniques, by 35%--420%. We have conducted a qualitative user study to evaluate the benefits of our framework in exploring group photo streams. The study indicates that users found the extracted clustering results clearly represent major themes in a group; the clustering results not only reflect how users describe the group data but often lead the users to discover the evolution of the group activity.

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  • (2023)Smart Multimedia Information RetrievalAnalytics10.3390/analytics20100112:1(198-224)Online publication date: 20-Feb-2023
  • (2018)Real-Time Multimedia Social Event Detection in MicroblogIEEE Transactions on Cybernetics10.1109/TCYB.2017.276234448:11(3218-3231)Online publication date: Nov-2018
  • (2018)Clustering Multivariate Time Series Data via Multi-Nonnegative Matrix Factorization in Multi-Relational NetworksIEEE Access10.1109/ACCESS.2018.28827986(74747-74761)Online publication date: 2018
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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications
ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 8, Issue 1
January 2012
149 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/2071396
Issue’s Table of Contents
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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 February 2012
Accepted: 01 August 2010
Revised: 01 April 2010
Received: 01 August 2009
Published in TOMM Volume 8, Issue 1

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Author Tags

  1. Social media
  2. multirelational learning
  3. nonnegative matrix factorization
  4. social network analysis
  5. structure mining

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Cited By

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  • (2023)Smart Multimedia Information RetrievalAnalytics10.3390/analytics20100112:1(198-224)Online publication date: 20-Feb-2023
  • (2018)Real-Time Multimedia Social Event Detection in MicroblogIEEE Transactions on Cybernetics10.1109/TCYB.2017.276234448:11(3218-3231)Online publication date: Nov-2018
  • (2018)Clustering Multivariate Time Series Data via Multi-Nonnegative Matrix Factorization in Multi-Relational NetworksIEEE Access10.1109/ACCESS.2018.28827986(74747-74761)Online publication date: 2018
  • (2018)Multi-modal multi-layered topic classification model for social event analysisMultimedia Tools and Applications10.1007/s11042-017-5588-777:18(23291-23315)Online publication date: 1-Sep-2018
  • (2018)Multivariate Time Series Clustering via Multi-relational Community Detection in NetworksWeb and Big Data10.1007/978-3-319-96890-2_12(138-145)Online publication date: 23-Jul-2018
  • (2017)Multi-modal Topic Modelling and Summarization with Dense Block Detection: A Review2017 International Conference on Recent Trends in Electrical, Electronics and Computing Technologies (ICRTEECT)10.1109/ICRTEECT.2017.45(177-182)Online publication date: Jul-2017
  • (2016)News Event Understanding by Mining Latent Factors From Multimodal TensorsProceedings of the 2016 ACM workshop on Vision and Language Integration Meets Multimedia Fusion10.1145/2983563.2983564(9-16)Online publication date: 16-Oct-2016
  • (2016)Multi-Modal Event Topic Model for Social Event AnalysisIEEE Transactions on Multimedia10.1109/TMM.2015.251032918:2(233-246)Online publication date: Feb-2016
  • (2015)A Coalition Formation Game Theory-Based Approach for Detecting Communities in Multi-relational NetworksWeb-Age Information Management10.1007/978-3-319-21042-1_3(30-41)Online publication date: 6-Jun-2015
  • (2014)Crowdsourcing changeProgress in Informatics10.2201/NiiPi.2014.11.2(3)Online publication date: Mar-2014
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