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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
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 | |||||||||||||||
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| Subjects: | H Social Sciences > HM Sociology Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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| 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: |
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| 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: |
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