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Home > Assessing Partisanship and Polarisation at Various Stages of News Production and Engagement

Assessing Partisanship and Polarisation at Various Stages of News Production and Engagement

Sat, 02/11/2024 - 21:37 — Snurb
Politics [1]
Polarisation [2]
Journalism [3]
Industrial Journalism [4]
Internet Technologies [5]
'Big Data' [6]
Social Media [7]
Facebook [8]
Social Media Network Mapping [9]
Twitter [10]
ARC Centre of Excellence for Automated Decision-Making and Society [11]
Dynamics of Partisanship and Polarisation in Online Public Debate (ARC Laureate Fellowship) [12]
AoIR 2024 [13]

I presented in and chaired the Saturday morning session at the AoIR 2024 [14] conference, which was on polarisation in news publishing and engagement, so no liveblogging this time. However, here are the slides from the three presentations that our various teams and I were involved in.

We started with my QUT DMRC colleague Laura Vodden, who discussed our plans for manual and automated content coding of news content for indicators of polarisation, and especially highlighted the surprising difficulties in getting access to quality and comprehensive news content data:

CHALLENGES IN ACQUIRING AND ANALYSING NEWS DATA AT SCALE.pptx [15] from tastysiltstone [16]

I presented the next paper, which explored the evidence for polarisation in news recommendations from Google News, building on our Australian Search Experience project in the ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S):

Polarisation via Search? Assessing the Political Spectrum of Google News Recommendations [17] from Axel Bruns [18]

The third paper was by my colleagues Felix Münch, Laura Vodden, and me, and explored potential signs of partisanship and polarisation in newssharing on Facebook and Twitter during 2021. We found limited evidence of such partisanship – but perhaps not outright polarisation – in public spaces on Facebook, but not on Twitter:

Polarisation In Newssharing: Reviewing the Evidence from Facebook and Twitter [19] from Axel Bruns [18]

Sadly I could not liveblog the wonderful presentations by Felix Gaisbauer and Michelle Riedlinger, since they were using my laptop – but hopefully they’ll be online somewhere too…

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Links
[1] http://snurb.info/taxonomy/term/47 [2] http://snurb.info/taxonomy/term/208 [3] http://snurb.info/taxonomy/term/81 [4] http://snurb.info/taxonomy/term/82 [5] http://snurb.info/taxonomy/term/7 [6] http://snurb.info/taxonomy/term/142 [7] http://snurb.info/taxonomy/term/125 [8] http://snurb.info/taxonomy/term/170 [9] http://snurb.info/taxonomy/term/84 [10] http://snurb.info/taxonomy/term/121 [11] http://snurb.info/taxonomy/term/182 [12] http://snurb.info/taxonomy/term/196 [13] http://snurb.info/taxonomy/term/222 [14] https://aoir.org/aoir2024/ [15] https://www.slideshare.net/slideshow/challenges-in-acquiring-and-analysing-news-data-at-scale-pptx/272953736 [16] https://www.slideshare.net/tastysiltstone [17] https://www.slideshare.net/slideshow/polarisation-via-search-assessing-the-political-spectrum-of-google-news-recommendations/272950391 [18] https://www.slideshare.net/Snurb [19] https://www.slideshare.net/slideshow/polarisation-in-newssharing-reviewing-the-evidence-from-facebook-and-twitter/272950448