World Wide Web

, Volume 20, Issue 2, pp 267–290

Twitter summarization with social-temporal context

  • Ruifang He
  • Yang Liu
  • Guangchuan Yu
  • Jiliang Tang
  • Qinghua Hu
  • Jianwu Dang
Article

Abstract

Twitter is one of the most popular social media platforms for online users to create and share information. Tweets are short, informal, and large-scale, which makes it difficult for online users to find reliable and useful information, arising the problem of Twitter summarization. On the one hand, tweets are short and highly unstructured, which makes traditional document summarization methods difficult to handle Twitter data. On the other hand, Twitter provides rich social-temporal context beyond texts, bringing about new opportunities. In this paper, we investigate how to exploit social-temporal context for Twitter summarization. In particular, we provide a methodology to model temporal context globally and locally, and propose a novel unsupervised summarization framework with social-temporal context for Twitter data. To assess the proposed framework, we manually label a real-world Twitter dataset. Experimental results from the dataset demonstrate the importance of social-temporal context in Twitter summarization.

Keywords

Twitter summarization Social media Time point detection Wavelet denoising Social-temporal context 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Ruifang He
    • 1
  • Yang Liu
    • 2
  • Guangchuan Yu
    • 1
  • Jiliang Tang
    • 3
  • Qinghua Hu
    • 1
  • Jianwu Dang
    • 1
  1. 1.Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.School of Computer Science and TechnologyPeking UniversityPekingChina
  3. 3.School of Computing, Informatics, and Decision Systems EngineeringArizona State UniversityTempeUSA

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