Learning influence probabilities and modelling influence diffusion in twitter

Zizhu Zhang, Weiliang Zhao, Jian Yang, Cecile Paris, Surya Nepal

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Influence diffusion has been widely studied in social networks for applications such as service promotion and marketing. There are two challenging issues here: (1) how we measure people's influence on others; (2) how we predict whom would be influenced by a particular person and when people would be influenced. Existing works have not captured the temporal and structural characteristics of influence diffusion in Twitter. In this paper, we firstly develop a model to learn influence probabilities between users in Twitter from their action history; secondly, we introduce diffusion models that are used to predict how information is propagated in Twitter. Experiment results show that our proposed models outperform existing models in terms of the balanced precision and recall.

Original languageEnglish
Title of host publicationWWW '19 Companion Proceedings of the 2019 World Wide Web Conference
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages1087-1094
Number of pages8
ISBN (Electronic)9781450366755
DOIs
Publication statusPublished - 13 May 2019
Event2019 World Wide Web Conference, WWW 2019 - San Francisco, United States
Duration: 13 May 201917 May 2019

Conference

Conference2019 World Wide Web Conference, WWW 2019
CountryUnited States
CitySan Francisco
Period13/05/1917/05/19

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Keywords

  • Depth decay
  • Influence diffusion
  • Influence probability
  • Time decay
  • Twitter

Cite this

Zhang, Z., Zhao, W., Yang, J., Paris, C., & Nepal, S. (2019). Learning influence probabilities and modelling influence diffusion in twitter. In WWW '19 Companion Proceedings of the 2019 World Wide Web Conference (pp. 1087-1094). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/3308560.3316701