Dual Public Opinion Guidance Model in Social Network
Authors: 
Binyan Lyu, Yajun Du, Peng JiaAuthors Info & Claims
Binyan Lyu, Yajun Du, Peng JiaAuthors Info & ClaimsPages 109 - 115
Abstract
Public opinion in online social network is currently an important topic related to our life closely, however, the research on public opinion guidance lacks a theoretical system and leveraging machine learning to deal with public opinion guidance is even minimal, so it is critical to study the control, guidance and the evolution of public opinion and to use machine learning to study the guidance of public opinion. In the paper, we proposed a public opinion guidance model based on dual learning, hereinafter referred to as the dual-POG model. Firstly, we use the Girvan and Newman (GN) algorithm to divide communities in social network and detect the opinion leaders. Secondly, apply the dual learning to construct the public opinion guidance model which is the main idea of the dual guidance mechanism we proposed to guide the leaders. Finally, we guide the remaining nodes based on opinion dynamics. This study applies machine learning to guide the online public opinion, which has an important significance for current public opinion development. The experiments demonstrate beneficial effects of the model of dual-POG. Comparison experiments indicate that the proposed approach outperforms the other methods.
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Published In
September 2021
189 pages
ISBN:9781450385091
DOI:10.1145/3490322
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Published: 27 December 2021
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- Research
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- the National Natural Science Foundation
- the Sichuan Regional Innovation Cooperation Project
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ICBDT 2021
ICBDT 2021: 2021 4th International Conference on Big Data Technologies
September 24 - 26, 2021
Zibo, China
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