This document summarizes a study on news sharing on Twitter in multiple countries. The researchers tracked the most shared news domains from Australia, Germany, Norway, Sweden, Denmark and Finland to analyze patterns of news sharing. They found that some sites had overlapping user bases while others attracted more distinct audiences. For example, in Australia the ABC and Guardian sites shared over 50% of their Twitter user bases. The researchers also analyzed differences between countries, like the prominence of tabloid sites in Germany. They discussed opportunities for further analysis, like comparing sharing behaviors over time and across other social platforms.

1. News Sharing on Twitter:
A Nationally Comparative Study
Axel Bruns, Brenda Moon, Felix Münch
Digital Media Research Centre
Queensland University of Technology, Brisbane
Jan-Hinrik Schmidt, Lisa Merten
Hans-Bredow-Institut für Medienforschung
an der Universität Hamburg
Hallvard Moe
Department of Information Science and Media Studies
University of Bergen
Sander Schwartz
Department of Digital Culture and Mobile Communication
IT University Copenhagen

2. Introduction
• News sharing:
– Major practice across social
media platforms
– 2016 Reuters Institute Digital
News Report:
• 31% ‘proactive participators’
• 21% ‘reactive participators’
– ‘Social filtering’, reaching beyond
traditional news audiences
– But also concerns about what
content shared
– And warnings about filter bubbles /
echo chambers
(Newman et al., 2016)

3. Methods
• Tracking news sharing on Twitter:
– Twitter search API returns tweets containing specific domains (e.g. abc.net.au
even if the URL has been shortened)
– Used for long-term tracking of leading news sites in Australia, Germany,
Norway, Sweden, Denmark, Finland – ATNIX, DETNIX, NOTNIX
– Data gathering using yourTwapperkeeper (using search API)
• some disruptions through server maintenance
– URL resolving and data cleaning using Gawk scripts
– Data analysis using Tableau and Google BigQuery

4. Data Selection
• Representative periods:
– March / April 2015
– September / October 2015
– March / April 2016
• Five most widely shared sites in each country
– dw.de removed (changed to dw.com)

5. Overall Market Shares per Country

6. Most Visited Sites
1
2
2
1
2
1
2
1
2
1 2
1
(Hitwise) (IVW)
(Reuters Institute Digital News Report 2016)

7. Public Service Media

8. Tabloids

9. Country Patterns: Australia
~2.7m ~2.7m ~2.3m

10. Country Patterns: Germany
Panama
Papers
AdBlock ban
~2.1m* ~2.9m ~2.9m

11. Country Patterns: Nordic Countries
~1m* ~1.5m ~1.2m

12. Understanding Userbase Overlaps
• How loyal or promiscuous are news sharers? How monocultural?
– Sharing activities could show strong left/right or quality/tabloid distinctions
– Single-site sharing could mean echo chambers / filter bubbles do exist
• Analytical choices:
– Selection of top five sites in each country
– Removal of users who did not share at least two links to a site during the six months
(March/April 2015, September/October 2015, April/March 2016)
– Calculation of userbase overlaps from site to site – this is directional:
• E.g. 10,000 users share site A; of these 2,000 share site B  overlap is 2,000 (20%)
• But 5,000 users share site B; of these 2,000 share site A  overlap is 2,000 (40%)
– No attempt yet to weight results by volume of user activity (e.g. heavy vs. light sharers)

13. Userbase Overlaps: Nordic Countries
Node size: indegree
Node colour: outdegree
Edge weight: percentage of source site’s
sharers who also share target site

14. Userbase Overlaps: Australia
56%
28%
30%
54%
45%
28%
28%
51%
30%
26%
Node size: indegree
Node colour: outdegree
Edge weight: percentage of source site’s
sharers who also share target site

15. Userbase Overlaps: Germany
29%
20%
33%
59%
56%
36%
19%
57%
Node size: indegree
Node colour: outdegree
Edge weight: percentage of source site’s
sharers who also share target site

16. Further Outlook
• News industry:
– Role of ‘official’ accounts / bots
– Themes / beats of news articles being shared
– Impact of ‘viral’ article styles (listicles, etc.)
– Site traffic generated by Twitter
• User activities:
– Activity around key events (e.g. Panama Papers)
– Weighting overlaps by strength of sharing activity
– Comparison with other social media platforms
• Country comparisons:
– Demographics of news sharers vs. overall Twittersphere vs. Internet users
– Cross-country sharing: cross-country publics?
– Topic dynamics over time / across countries (e.g. refugee crisis / Brexit / …)

17. Axel Bruns @snurb_dot_info
Brenda Moon @brendam
Felix Münch @flxvctr
Jan-Hinrik Schmidt @janschmidt
Lisa Merten @lisamerten
Hallvard Moe @halmoe
Sander Schwartz @54ndr