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Predicting Political Polarization from Cyberbalkanization: Time series analysis of Facebook pages and Opinion Poll during the Hong Kong Occupy Movement

Published: 28 June 2015 Publication History

Abstract

The purpose of this study is to investigate the temporal association between cyberbalkanization and real life polarization of public opinion during the Hong Kong Occupy Movement in 2014. 1,387 Facebook Pages about Hong Kong during July 1 to December 15, 2014 were collected, their publicly accessible posts were retrieved, and a post sharing network (1,397 nodes and 41,404 edges) was constructed. Network communities were computationally extracted to determine the community membership for each Facebook page. Daily degree of cyberbalkanization was quantified with the number of sharings through strong ties (intra-community sharing) connections. The level of political polarization was derived from the opinion polls data with the proportion of respondents who gave extreme ratings to the government leader in Hong Kong. In a time series analysis, the daily degree of cyberbalkanization, as measured by the number of sharing through the strong ties, was significantly associated with the level of political polarization, particularly with the younger age group's opinion poll result. This is the first study that provides empirical evidence for supporting cyberbalkanization to serve as a leading predictive indicator of the polarization of public opinion for at least 10 days ahead, suggesting that social media data analysis can supplement traditional public opinion research methods, such as phone survey, during social controversy.

References

[1]
Barberá, P. 2014. How social media reduces mass political polarization, evidence from Germany, Spain, and the US. working paper.
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Farrell, H. 2012. The consequences of the internet for politics. Annual Review of Political Science. 15, (2012), 35--52.
[3]
Hollander, B.A. 2008. Tuning out or tuning elsewhere? Partisanship, polarization, and media migration from 1998 to 2006. Journalism & Mass Communication Quarterly. 85, 1 (2008), 23--40.
[4]
Sunstein, C.R. 2009. Republic. com 2.0. Princeton University Press.
[5]
Wong, W. and Chan, S. 2015. You are what you read: How the digital divide changes the political views of people in hong kong. {In chinese}. Ming Pao. (January 17 2015).

Cited By

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  • (2020)A Survey on Computational PoliticsIEEE Access10.1109/ACCESS.2020.30349838(197379-197406)Online publication date: 2020
  • (2018)What Predicts Selective Avoidance on Social Media? A Study of Political Unfriending in Hong Kong and TaiwanAmerican Behavioral Scientist10.1177/000276421876425162:8(1097-1115)Online publication date: 19-Mar-2018

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  1. Predicting Political Polarization from Cyberbalkanization: Time series analysis of Facebook pages and Opinion Poll during the Hong Kong Occupy Movement

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    cover image ACM Conferences
    WebSci '15: Proceedings of the ACM Web Science Conference
    June 2015
    366 pages
    ISBN:9781450336727
    DOI:10.1145/2786451
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    New York, NY, United States

    Publication History

    Published: 28 June 2015

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    Author Tags

    1. cyberbalkanization
    2. political polarization
    3. social media

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    • Research-article
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    • Refereed limited

    Funding Sources

    • Hong Kong Government
    • HKU SPACE Postgraduate Fund

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    WebSci '15
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    WebSci '15: ACM Web Science Conference
    June 28 - July 1, 2015
    Oxford, United Kingdom

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    Overall Acceptance Rate 245 of 933 submissions, 26%

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    Cited By

    View all
    • (2020)A Survey on Computational PoliticsIEEE Access10.1109/ACCESS.2020.30349838(197379-197406)Online publication date: 2020
    • (2018)What Predicts Selective Avoidance on Social Media? A Study of Political Unfriending in Hong Kong and TaiwanAmerican Behavioral Scientist10.1177/000276421876425162:8(1097-1115)Online publication date: 19-Mar-2018

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