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With the popularity of new media, the relationship between the media and the public has changed considerably. A comprehensive and quantitative analysis of the relationship between the public and media can reveal the development of news media in China and help provide some constructive advice to improve their quality. Therefore, we establish vector autoregression (VAR) models between the media and the public on 160 network trending events from 2011 to 2018 based on the Baidu Index. In specific terms, we explore the causal relationship between them with the Granger causality test and analyze the dynamic effects using the impulse response function (IRF) analysis. Our findings suggest that there are satisfactory two-way interactions between the news media and the public in China (especially in the event categories directly related to people’s livelihood, such as safety misadventure, natural disasters, food and drug safety, and entertainment) and that the public plays a leading role for most events. We also find that the news media lags behind users generally, which prompts us to propose the three-relay hypothesis to explain its dissemination mechanism. Also, the impulse responses of attention to negative events are generally more drastic than those to positive events. For hot network events in China, the time span and sample size of our research are large, increasing the results’ reliability.


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A Quantitative Analysis of the Relationship Between the Public and News Media Attentions to Hot Network Events in China

Show Author's information Jiaqing Liu1,2Yue Wang2( )Sha He3Wuyue Shangguan4Tianmei Wang2
School of Information, Renmin University of China, Beijing 100872, China
School of Information, Central University of Finance and Economics, Beijing 100081, China
LinkedIn Inc., Sunnyvale, CA 94085, USA
School of Management, Xiamen University, Xiamen 361005, China

Abstract

With the popularity of new media, the relationship between the media and the public has changed considerably. A comprehensive and quantitative analysis of the relationship between the public and media can reveal the development of news media in China and help provide some constructive advice to improve their quality. Therefore, we establish vector autoregression (VAR) models between the media and the public on 160 network trending events from 2011 to 2018 based on the Baidu Index. In specific terms, we explore the causal relationship between them with the Granger causality test and analyze the dynamic effects using the impulse response function (IRF) analysis. Our findings suggest that there are satisfactory two-way interactions between the news media and the public in China (especially in the event categories directly related to people’s livelihood, such as safety misadventure, natural disasters, food and drug safety, and entertainment) and that the public plays a leading role for most events. We also find that the news media lags behind users generally, which prompts us to propose the three-relay hypothesis to explain its dissemination mechanism. Also, the impulse responses of attention to negative events are generally more drastic than those to positive events. For hot network events in China, the time span and sample size of our research are large, increasing the results’ reliability.

Keywords:

vector autoregression model, Granger causality test, impulse response function, hot network event, public attention, media attention
Received: 19 September 2021 Revised: 31 March 2022 Accepted: 06 April 2022 Published: 30 June 2022 Issue date: June 2022
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Publication history

Received: 19 September 2021
Revised: 31 March 2022
Accepted: 06 April 2022
Published: 30 June 2022
Issue date: June 2022

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© The author(s) 2022

Acknowledgements

Acknowledgment

This work was supported by the National Natural Science Foundation of China (No. 72072194), National Social Science Foundation of China (No. 14AZD045), and Discipline Construction Foundation of the Central University of Finance and Economics.

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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