World Wide Web

, Volume 20, Issue 1, pp 61–87 | Cite as

Using time-sensitive interactions to improve topic derivation in twitter

  • Robertus NugrohoEmail author
  • Weiliang Zhao
  • Jian Yang
  • Cecile Paris
  • Surya Nepal


Twitter has become one of the most popular social media platforms, widely used for discussion and information dissemination on all kinds of topics. As a result, both business and academics have researched methods to identify the topics being discussed on Twitter. Those methods can be employed for a number of applications, including emergency management, advertisements, and corporate/government communication. However, deriving topics from this short text based and highly dynamic environment remains a huge challenge. Most current methods use the content of tweets as the only source for topic derivation. Recently, tweet interactions have been considered for improving the quality of topic derivation. In this paper, we propose a method that considers both content and interactions with a temporal aspect to further improve the quality of topic derivation. The impact of the temporal aspect in user/tweet interactions is analyzed based on several Twitter datasets. The proposed method incorporates time when it clusters tweets and identifies representative terms for each topic. Experimental results show that the inclusion of the temporal aspect in the interactions results in a significant improvement in the quality of topic derivation comparing to existing baseline methods.


Topic derivation Temporal aspect in twitter Joint matrix factorization 



This work is partially supported by the Indonesian Directorate General of Higher Education (DGHE), Macquarie University, CSIRO Data61, Australian Research Council LP120200231, and Australian Research Council DP140101369.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of ComputingMacquarie University and CSIRO Data61SydneyAustralia
  2. 2.Department of ComputingMacquarie UniversitySydneyAustralia
  3. 3.CSIRO Data61 AustraliaSydneyAustralia

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