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Research paper | Open Access

The effect of online political deliberation on the effectiveness of government response

Yuning Zhao1( )Xinxue Zhou1Tianmei Wang2
Central University of Finance and Economics, Beijing, China
School of Information, Central University of Finance and Economics, Beijing, China
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Abstract

Purpose

Following Hovland's persuasion theory, this paper aims to develop a conceptual model and analyzes characteristics of online political deliberation behavior from three aspects (i.e. information, situation and manager). Based on the whole interactive process of online political deliberation, this paper aims to reveal the key points that affect the response effect of the government from the persuasive perspective of online political consultation.

Design/methodology/approach

Based on more than 40,000 netizens’ posts and government responses from 2011 to the first half of 2019 of the Chinese political platform, this paper used the text analysis and machine learning methods to extract measurement variables of online political deliberation characteristics and the econometricsanalysis method to conduct empirical research.

Findings

The results showed that the textual information, political environment and identity of the political objects affect the effectiveness of government response. Furthermore, for different position categories of political officials, the length of political texts, topic categories and emotional tendencies have different effects on the response effectiveness. Additionally, the effect of political time on the effectiveness of response differs.

Originality/value

The findings will help ascertain the characteristics of online political deliberation behavior that affect how effective government response is and provide a theoretical basis for why the public should express their political concerns.

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International Journal of Crowd Science
Pages 309-331
Cite this article:
Zhao Y, Zhou X, Wang T. The effect of online political deliberation on the effectiveness of government response. International Journal of Crowd Science, 2020, 4(3): 309-331. https://doi.org/10.1108/IJCS-04-2020-0009

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Received: 17 April 2020
Revised: 23 May 2020
Accepted: 24 May 2020
Published: 14 July 2020
© The author(s)

Yuning Zhao, Xinxue Zhou and Tianmei Wang. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

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