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SCHC: Incorporating Social Contagion and Hashtag Consistency for Topic-Oriented Social Summarization

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Database Systems for Advanced Applications (DASFAA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12682))

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Abstract

The boom of social media platforms like Twitter brings the large scale short, noisy and redundant messages, making it difficult for people to obtain essential information. We study extractive topic-oriented social summarization to help people grasp the core information on social media quickly. Previous methods mainly extract salient content based on textual information and shallow social signals. They ignore that user generated messages propagate along the social network and affect users on their dissemination path, leading to user-level redundancy. Besides, hashtags on social media are a special kind of social signals, which can be regarded as keywords of a post and contain abundant semantics. In this paper, we propose to leverage social theories and social signals (i.e. multi-order social relations and hashtags) to address the redundancy problem and extract diverse summaries. Specifically, we propose a novel unsupervised social summarization framework which considers Social Contagion and Hashtag Consistency (SCHC) theories. To model relations among tweets, two relation graphs are constructed based on user-level and hashtag-level interaction among tweets. These social relations are further integrated into a sparse reconstruction framework to alleviate the user-level and hashtag-level redundancy respectively. Experimental results on the CTS dataset prove that our approach is effective.

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Notes

  1. 1.

    http://www.yelab.net/software/SLEP/.

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Acknowledgement

We thank the anonymous reviewers for their valuable feedback. Our work is supported by the National Natural Science Foundation of China (61976154), the National Key R&D Program of China (2019YFC1521200), the Tianjin Natural Science Foundation (18JCYBJC15500), and the State Key Laboratory of Communication Content Cognition, People’s Daily Online (No.A32003).

Author information

Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Ruifang He, Huanyu Liu & Liangliang Zhao

  2. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, 300350, China

    Ruifang He, Huanyu Liu & Liangliang Zhao

  3. State Key Laboratory of Communication Content Cognition, People’s Daily Online, Beijing, 100733, China

    Ruifang He

Authors
  1. Ruifang He
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  2. Huanyu Liu
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Corresponding author

Correspondence to Ruifang He .

Editor information

Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Christian S. Jensen

  2. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  4. The Pennsylvania State University, University Park, PA, USA

    Wang-Chien Lee

  5. National Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  6. Athens University of Economics and Business, Athens, Greece

    Vana Kalogeraki

  7. National Cheng Kung University, Tainan City, Taiwan

    Jen-Wei Huang

  8. National Tsing Hua University, Hsinchu, Taiwan

    Chih-Ya Shen

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He, R., Liu, H., Zhao, L. (2021). SCHC: Incorporating Social Contagion and Hashtag Consistency for Topic-Oriented Social Summarization. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12682. Springer, Cham. https://doi.org/10.1007/978-3-030-73197-7_44

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  • DOI: https://doi.org/10.1007/978-3-030-73197-7_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73196-0

  • Online ISBN: 978-3-030-73197-7

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