Paper by Axel Bruns and Tobias Keller, presented at the Social Media & Society 2020 conference, 22 July 2020. A video of the presentation is here: https://www.youtube.com/watch?v=pCKpDkC8iqI.

1. CRICOS No.00213J
News Diffusion on Twitter:
Comparing the Dissemination Careers
for Mainstream and Marginal News
Axel Bruns and Tobias Keller
a.bruns@qut.edu.au / tobias.keller@qut.edu.au

2. CRICOS No.00213J
Background
• ‘True’ and ‘false’ news:
• “Lies spread faster than the truth” (Science tagline)
• “falsehood diffused significantly farther, faster, deeper, and
more broadly than the truth” (p. 1)
• “it took the truth about six times as long as falsehood to
reach 1500 people” (p. 3)
• But: only retweet cascades that received an @reply linking
to a fact-checking site (supp. mat. p. 11)
• Limited generalisability:
• Only fact-checked stories – what about ordinary,
noncontroversial news?
• Retweet cascades – what about link sharing?
• Aggregate patterns – what about site-by-site differences?
• 2006-2017 timeframe – what about evolution in practices? Vosoughi, S., Roy, D., & Aral, S. (2018). The Spread of True and False News Online.
Science, 359, 1146–1151. https://doi.org/10.1126/science.aap9559

3. CRICOS No.00213J
Aims
• News dissemination careers:
• How quickly do stories from mainstream and fringe news outlets reach their Twitter audiences?
• Are there systematic differences between outlets (and/or outlet types)?
• Is there evidence of this being affected by coordinated (in)authentic activities?
• e.g. sockpuppeting: multiple ‘independent’ accounts retweeting a central account immediately
• e.g. astroturfing: multiple ‘independent’ accounts posting the same links at the same time

4. CRICOS No.00213J
Data
• Data sources:
• Australian Twitter News Index (ATNIX):
• Any tweets linking to an article in one of ~35 leading Australian news outlets
• Fake News Index (FakeNIX):
• Any tweets linking to an article in one of ~1400 fringe news sites listed in one or more public lists of dubious sources
(e.g. Hoaxy, Melissa Zimdars, Guess et al. 2018/2019, …)
• Data selection:
• Outlets:
• Four of the most shared ATNIX outlets: ABC News (Australia), Sydney Morning Herald, news.com.au, The Conversation
• Ten of the most shared FakeNIX outlets:
Breitbart, Gateway Pundit, Daily Beast, Raw Story, Daily Caller, Russia Today (RT), Washington Examiner, Sputnik News, Judicial Watch, Daily Wire
• Russia Today and Sputnik News: distinction between different language versions (e.g. arabic.rt.com, tr.sputniknews.com)
• Timeframe:
• First tweet sharing article during January to October 2019; all subsequent tweets for two months after the first tweet (i.e. to the end of 2019 at most)
• Reach:
• Any articles with at least 200 shares by two months after first post
• ATNIX: 4,141 stories, shared in 2,365,537 tweets
• FakeNIX: 31,868 stories, shared in 33,552,184 tweets

5. CRICOS No.00213J
Process

6. CRICOS No.00213J
Dissemination Careers
• Approach:
• For each individual story:
• t0: timestamp of the first tweet sharing the story URL (between Jan. and Oct. 2019)
• tmax: timestamp of the final tweets sharing the story URL (max. two months after first tweet)
• s(tmax): total number of tweets that shared the story URL by tmax
• v(tn): percentage of total share count achieved by timestamp tn — v(tn) = s(tn)/s(tmax)
• Per site:
• Average v(tn) across all stories, for each point n ≥ 0 and n ≤ 86,400 (two months)
Average dissemination careers per site

7. CRICOS No.00213J
60 mins. x 24 hours x 60 days
= 86,400 mins.
0 mins.: first recorded tweet
sharing the story URL
100%: total count of tweets
sharing the URL after 60 days
Note: logarithmic scale to better
show early sharing patterns

8. CRICOS No.00213J
Quick and possibly short-lived stories
Slow, sleeper stories

9. CRICOS No.00213J

10. CRICOS No.00213J
Findings

11. CRICOS No.00213J

12. CRICOS No.00213J
ATNIX sites mainly in midfield

13. CRICOS No.00213J
Raw Story, Gateway Pundit, and
some Russian state media sites in Turkish,
Spanish, Arabic disseminate most quickly

14. CRICOS No.00213J
Specialist sites shared more slowly:
The Conversation – scholarly contributions
Judicial Watch – hyperpartisan lawfare

15. CRICOS No.00213J
Domain Mins. to 10% Mins. to 25% Mins. to 50% Mins. to 75%
ATNIX sites abc.net.au 79 234 643 1,592
news.com.au 133 353 863 1,817
smh.com.au 144 393 818 1,505
theconversation.com 233 779 2,045 6,599
FakeNIX sites breitbart.com 87 228 617 1,265
dailycaller.com 57 202 641 1,343
dailywire.com 48 181 712 1,508
judicialwatch.org 138 1,129 4,973 15,661
rawstory.com 14 57 209 634
rt.com 31 157 611 1,411
sputniknews.com 68 280 873 2,199
thedailybeast.com 41 176 552 1,301
thegatewaypundit.com 27 88 289 792
washingtonexaminer.com 93 328 954 2,087
Time to Selected Milestones

16. CRICOS No.00213J
Domain Mins. to 10% Mins. to 25% Mins. to 50% Mins. to 75%
ATNIX sites abc.net.au 79 234 643 1,592
news.com.au 133 353 863 1,817
smh.com.au 144 393 818 1,505
theconversation.com 233 779 2,045 6,599
FakeNIX sites breitbart.com 87 228 617 1,265
dailycaller.com 57 202 641 1,343
dailywire.com 48 181 712 1,508
judicialwatch.org 138 1,129 4,973 15,661
rawstory.com 14 57 209 634
rt.com 31 157 611 1,411
sputniknews.com 68 280 873 2,199
thedailybeast.com 41 176 552 1,301
thegatewaypundit.com 27 88 289 792
washingtonexaminer.com 93 328 954 2,087
Time to Selected Milestones

17. CRICOS No.00213J
Social Bots and Automated Dissemination
Methods
• Machine learning algorithms based on different training data
• Tweetbotornot2 (Kearny, 2020)
• Botometer v3 (Varol et al., 2018)
• False positive and other problems (Rauchfleisch & Kaiser, 2020, Grimme et al., 2018)
Data
• Stratified random sample of 10,000 unique Twitter accounts per outlet (total N = 150,000)
• Density plot comparison
• Exploratory manual analysis

18. CRICOS No.00213J
1. Almost no
(fully)
automated
accounts
sharing news.
2. Overall, less
than 1% of
accounts
received a
score higher
than 0.5.
3. Almost no
difference
between
mainstream and
marginal news
sites.

19. CRICOS No.00213J

20. CRICOS No.00213J

21. CRICOS No.00213J
Key Observations and Further Outlook
• Overall:
• Mis-/disinformation and fringe news doesn’t necessarily disseminate faster than ‘real’, mainstream news
• Substantial differences between different types of sites in either category
• Speed of dissemination likely linked mainly to type of news coverage and intended audience
• Dissemination can be affected by (authentic or inauthentic) coordinated activities
• Very few automated accounts overall – no major differences between mainstream and fringe news sites
• Possibly more bots in disguise, and genuine hyperpartisan supporters, spreading fringe news
• Next steps:
• Current study limited to major stories from major Australian mainstream / US fringe media sites during 2019
• Plan to extend analysis to broader range of sites, stories, and different kinds of bots
• Patterns may look different during times of heightened activity – e.g. bushfires, COVID-19 crisis
• Combination of time-series and network analysis and close reading required to reveal full picture

22. CRICOS No.00213J
This research is funded by the Australian Research Council projects
DP200101317 Evaluating the Challenge of ‘Fake News’ and Other
Malinformation and FT130100703 Understanding Intermedia Information
Flows in the Australian Online Public Sphere, and by the Swiss National
Science Foundation postdoc mobility grant P2ZHP1_184082 Political Social
Bots in the Australian Twittersphere.
Computational resources and services used in this work were provided by
the QUT eResearch Office, Division of Research and Innovation.
Acknowledgments