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Visual topic discovering, tracking and summarization from social media streams

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Abstract

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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Acknowledgments

This research was supported in part by National Key Technology Support Program (No. 2015BAH01F02), in part by Science and Technology Commission of Shanghai Municipality (No.16511102702 and No.14DZ2260800). The authors gratefully acknowledge the support of Yu-Ru Lin’s Lab at University of Pittsburgh, supported in part by NSF grant #1423697, #1634944, and the CRDF and CIS. Any opinions, findings, and conclusions or recommendations expressed in this material do not necessarily reflect the views of the funding sources.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, 500 Dongchuan Road, 200241, Shanghai, China

    Zhao Lu & Xiaoxia Huang

  2. School of Information Sciences, University of Pittsburgh, 135 North Bellefield Ave., PA, 15260, Pittsburgh, USA

    Yu-Ru Lin

  3. Department of Business and Computer Science, Southwestern Oklahoma State University, 100 Campus Drive, Weatherford, OK, 73096, USA

    Naixue Xiong

  4. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, 333 Longteng Road, 201620, Shanghai, China

    Zhijun Fang

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  1. Zhao Lu
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  2. Yu-Ru Lin
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  3. Xiaoxia Huang
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  4. Naixue Xiong
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Correspondence to Zhao Lu.

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Lu, Z., Lin, YR., Huang, X. et al. Visual topic discovering, tracking and summarization from social media streams. Multimed Tools Appl 76, 10855–10879 (2017). https://doi.org/10.1007/s11042-016-3877-1

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  • DOI: https://doi.org/10.1007/s11042-016-3877-1

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