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.
This is a Powerpoint about research into the codes and conventions of a film ...
News Diffusion on Twitter: Comparing the Dissemination Careers for Mainstream and Marginal News
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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
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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
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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
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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
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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
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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
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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
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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.
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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
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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