Modeling user posting behavior on social media

  • Authors:
  • Zhiheng Xu;Yang Zhang;Yao Wu;Qing Yang

  • Affiliations:
  • Institute of Automation, Chinese Academy of Sciences, beijing, China;Institute of Automation, Chinese Academy of Sciences, beijing, China;Institute of Automation, Chinese Academy of Sciences, beijing, China;Institute of Automation, Chinese Academy of Sciences, beijing, China

  • Venue:
  • SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2012

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Abstract

User generated content is the basic element of social media websites. Relatively few studies have systematically analyzed the motivation to create and share content, especially from the perspective of a common user. In this paper, we perform a comprehensive analysis of user posting behavior on a popular social media website, Twitter. Specifically, we assume that user behavior is mainly influenced by three factors: breaking news, posts from social friends and user's intrinsic interest, and propose a mixture latent topic model to combine all these factors. We evaluated our model on a large-scale Twitter dataset from three different perspectives: the perplexity of held-out content, the performance of predicting retweets and the quality of generated latent topics. The results were encouraging, our model clearly outperformed its competitors.