Please use this identifier to cite or link to this item:
Title: Modeling opinion influence with user dual identity
Authors: Chen, C 
Wang, Z 
Li, W 
Keywords: Dual identity
Joint learning
Opinion influence modeling
Issue Date: 2017
Publisher: Association for Computing Machinery
Source: International Conference on Information and Knowledge Management, Proceedings, 2017, v. Part F131841, p. 2019-2022 How to cite?
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.
Description: 26th ACM International Conference on Information and Knowledge Management, CIKM 2017, Pan Pacific, Singapore, 6-10 November 2017
ISBN: 9781450349185
DOI: 10.1145/3132847.3133125
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Citations as of Dec 17, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.