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Visual topic discovering, tracking and summarization from social media streams

Lu, Z and Lin, YR and Huang, X and Xiong, N and Fang, Z (2017) Visual topic discovering, tracking and summarization from social media streams. Multimedia Tools and Applications, 76 (8). 10855 - 10879. ISSN 1380-7501

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Abstract

© 2016, Springer Science+Business Media New York. Nowadays, microblogging has become popular, with hundreds of millions of short messages being posted and shared every minute on a variety of topics in social media such as Facebook, Twitter and Weibo. Many of such messages contain videos that captured particular events or moments in people’s life. In this work, we seek to automatically identify the video topics posted in the social media streams on Weibo. While Topic Detection and Tracking (TDT) task has been extensively studied in multimedia retrieval, automatically discovering, tracking and summarizing video topics from social media streams is still challenging due to short and noisy content, diverse and fast changing topics, and large data volume. In this paper, we propose a K-partite graph based approach to address these challenges. We introduce a K-partite graph representation to simultaneously model the relationships among videos contained in the Weibo streams, their textural features and visual features. We propose a novel joint clustering algorithm to capture global structure of the K-partite graph in a “relation cluster network” (RCN) where latent, meta-nodes are added to the network to represent video clusters. Based on this network we propose methods for tracking and summarizing the videos in streams through fusing various types of features and multiple ranking schemes. The experiment results based on a real dataset show the effectiveness of our method with significant improvement over baseline.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Lu, Z
Lin, YRyurulin@pitt.eduYURULIN
Huang, X
Xiong, N
Fang, Z
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
CorrespondentLin, Yu-Ruyurulin@pitt.eduYURULINUNSPECIFIED
Date: 1 April 2017
Date Type: Publication
Journal or Publication Title: Multimedia Tools and Applications
Volume: 76
Number: 8
Page Range: 10855 - 10879
DOI or Unique Handle: 10.1007/s11042-016-3877-1
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 1380-7501
Date Deposited: 26 Jun 2017 16:28
Last Modified: 13 Oct 2017 22:59
URI: http://d-scholarship.pitt.edu/id/eprint/32552

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