Modeling opinion influence with user dual identity

Chengyao Chen, Zhitao Wang, Wenjie Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Exploring the mechanism that explains howa user's opinion changes under the influence of his/her neighbors is of practical importance (e.g., for predicting the sentiment of his/her future opinion) and has attracted wide attention from both enterprises and academics. Though various opinion influence models have been proposed for opinion prediction, they only consider users' personal identities, but ignore their social identities with which people behave to fit the expectations of the others in the same group. In this work, we explore users' dual identities, including both personal identities and social identities to build a more comprehensive opinion influence model for a better understanding of opinion behaviors. A novel joint learning framework is proposed to simultaneously model opinion dynamics and detect social identity in a unified model. The effectiveness of the proposed approach is demonstrated through the experiments conducted on Twitter datasets.
Original languageEnglish
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2019-2022
Number of pages4
VolumePart F131841
ISBN (Electronic)9781450349185
DOIs
Publication statusPublished - 6 Nov 2017
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Pan Pacific Singapore Hotel, Singapore, Singapore
Duration: 6 Nov 201710 Nov 2017

Conference

Conference26th ACM International Conference on Information and Knowledge Management, CIKM 2017
Country/TerritorySingapore
CitySingapore
Period6/11/1710/11/17

Keywords

  • Dual identity
  • Joint learning
  • Opinion influence modeling

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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