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Measuring Online Political Dialogue: Does Polarization Trigger More Deliberation?

Authors: Ignacio-Jesús Serrano-Contreras; Javier García-Marín; Óscar G. Luengo;

Measuring Online Political Dialogue: Does Polarization Trigger More Deliberation?

Abstract

In recent years, we have witnessed an increasing consolidation of different realms where citizens can deliberate and discuss a variety of topics of general interest, including politics. The comments on news posts in online media are a good example. The first theoretical contributions called attention to the potential of those spaces to build a <em>healthy </em>(civic and participatory) public sphere, going much deeper in the process of political dialogue and deliberation (Fung, Gilman, &amp; Shkabatur, 2013; Lilleker &amp; Jackson, 2008; O’Reilly, 2005; Stromer-Galley &amp; Wichowski, 2011). Polarization has been configured as a constant feature of the quality of the mentioned dialogues, particularly in Mediterranean countries (polarized pluralists’ cases). One of the research challenges at the moment has to do with the scrutiny of polarization within the political deliberation provoked by news stories. The goal of this article is the analysis of political dialogue from the perspective of the polarization in the increasingly popular network YouTube, which is presenting very particular characteristics. Using a sample of almost 400,000 posted comments about diverse topics (climate change, the Catalonian crisis, and Political parties’ electoral ads) we propose an automated method in order to measure polarization. Our hypothesis is that the number of comments (quantitative variable) is positively related to their polarization (qualitative variable). We will also include in the examination information about the ideological editorial line of newspapers, the type of topic under discussion, the amount of traceable dialogue, etc. We propose an index to (1) measure the polarization of each comment and use it to show how this value has behaved over time; and (2) verify the hypothesis using the average polarization of comments for each video.

Country
Spain
Keywords

Text mining, Politikwissenschaft, social media, text mining, Öffentlichkeit, algorithms, Polarisierung, youtube, Interactive, electronic Media, Soziale Medien, Polarization, Political Process, Elections, Political Sociology, Political Culture, Political science, interaktive, elektronische Medien, News media, journalism, publishing, politische Willensbildung, politische Soziologie, politische Kultur, Online-Medien, polarization, algorithm, YouTube, Communication. Mass media, the public, online media, P87-96, Algorithmus, Meinungsforschung, public sphere, Public sphere, YouTube; text mining, Publizistische Medien, Journalismus,Verlagswesen, Social Media, Public Opinion Research, Algorithms, ddc: ddc:070, ddc: ddc:320

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    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Top 10%
Top 10%
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gold