News Sharing on Twitter: A Nationally Comparative StudyAxel Bruns, Brenda Moon, Felix MünchDigital Media Research CentreQueensland University of Technology, Brisbane Jan-Hinrik Schmidt, Lisa MertenHans-Bredow-Institut für Medienforschung an der Universität Hamburg

Hallvard MoeDepartment of Information Science and Media StudiesUniversity of Bergen Sander SchwartzDepartment of Digital Culture and Mobile CommunicationIT University Copenhagen

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)

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

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)

Overall Market Shares per Country

Most Visited Sites

12

2

1

21

21

21 2

1

(Hitwise) (IVW)

(Reuters Institute Digital News Report 2016)

Public Service Media

Tabloids

Country Patterns: Australia

~2.7m ~2.7m ~2.3m

Country Patterns: Germany

Panama Papers

AdBlock ban

~2.1m* ~2.9m ~2.9m

Country Patterns: Nordic Countries

~1m* ~1.5m ~1.2m

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)

Userbase Overlaps: Nordic CountriesNode size: indegreeNode colour: outdegreeEdge weight: percentage of source site’s

sharers who also share target site

Userbase Overlaps: Australia

56%

28%

30%

54%

45%28%

28%

51%

30%

26%

Node size: indegreeNode colour: outdegreeEdge weight: percentage of source site’s

sharers who also share target site

Userbase Overlaps: Germany

29%

20%

33%

59%

56%36%

19%

57%

Node size: indegreeNode colour: outdegreeEdge weight: percentage of source site’s

sharers who also share target site

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 / …)

Axel Bruns @snurb_dot_infoBrenda Moon @brendamFelix Münch @flxvctrJan-Hinrik Schmidt @janschmidtLisa Merten @lisamertenHallvard Moe @halmoeSander Schwartz @54ndr