Modeling online collective emotions through knowledge transfer

Saike He, Xiaolong Zheng, Dajun Zeng

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


Online emotion diffusion is a compound process that involves interactions with multiple modalities. For instance, different behaviors influence the velocity and scale of emotion diffusion in online communities. Depicting and predicting massive online emotions helps to guide the trend of emotion evolution, thus avoiding unprecedented damages in crises. However, most existing work tries to depict and predict online emotions based on models not considering related modalities. There still lacks an efficient modeling framework that promotes performance by leveraging multi-modality knowledge, and quantifies the interactions among different modalities. In this paper, we elaborate a computational model to jointly depict online emotions and behaviors. By introducing a common structure, we can quantify how user emotions interact with the corresponding behaviors. To scale up to large dataset, we propose a hierarchical optimization algorithm to accelerate the convergence of the model. Evaluation on Sina Weibo dataset suggests that prediction error rate is lowered by 69 percent with the proposed model. In addition, the proposed model helps to explain how user emotions influence consequent behaviors in extreme situations.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Intelligence and Security Informatics
Subtitle of host publicationSecurity and Big Data, ISI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages3
ISBN (Electronic)9781509067275
StatePublished - Aug 8 2017
Externally publishedYes
Event15th IEEE International Conference on Intelligence and Security Informatics, ISI 2017 - Beijing, China
Duration: Jul 22 2017Jul 24 2017


Other15th IEEE International Conference on Intelligence and Security Informatics, ISI 2017


  • knowledge transfer
  • online emotions
  • social crises

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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