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Neural opinion dynamics model for the prediction of user-level stance dynamics

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Zhu, Lixing, He, Yulan and Zhou, Deyu (2020) Neural opinion dynamics model for the prediction of user-level stance dynamics. Information Processing & Management, 57 (2). 102031. doi:10.1016/j.ipm.2019.03.010

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Official URL: https://doi.org/10.1016/j.ipm.2019.03.010

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Abstract

Social media platforms allow users to express their opinions towards various topics online. Oftentimes, users' opinions are not static, but might be changed over time due to the influences from their neighbors in social networks or updated based on arguments encountered that undermine their beliefs. In this paper, we propose to use a Recurrent Neural Network (RNN) to model each user's posting behaviors on Twitter and incorporate their neighbors' topic-associated context as attention signals using an attention mechanism for user-level stance prediction. Moreover, our proposed model operates in an online setting in that its parameters are continuously updated with the Twitter stream data and can be used to predict user's topic-dependent stance. Detailed evaluation on two Twitter datasets, related to Brexit and US General Election, justifies the superior performance of our neural opinion dynamics model over both static and dynamic alternatives for user-level stance prediction.

Item Type: Journal Article
Subjects: H Social Sciences > HM Sociology
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Social media, Internet users -- Attitudes -- Mathematical models, Twitter (Firm)
Journal or Publication Title: Information Processing & Management
Publisher: Elsevier
ISSN: 0306-4573
Official Date: March 2020
Dates:
DateEvent
March 2020Published
29 March 2019Available
26 March 2019Accepted
Volume: 57
Number: 2
Article Number: 102031
DOI: 10.1016/j.ipm.2019.03.010
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Grant number: 103652
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
2016YFC1306704[MSTPRC] Ministry of Science and Technology of the People's Republic of Chinahttp://dx.doi.org/10.13039/501100002855
61772132[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
BK20161430Natural Science Foundation of Jiangsu Provincehttp://dx.doi.org/10.13039/501100004608
103652Innovate UKhttp://dx.doi.org/10.13039/501100006041

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