This document summarizes a study comparing the dissemination of news articles on Twitter from mainstream and suspect news outlets. The study found: 1) Misinformation does not necessarily spread faster than real news, and there are differences in speed of dissemination between different types of outlets. 2) Dissemination can be strongly affected by coordinated activities like co-retweeting and co-tweeting of URLs, which was evidence of artificial boosting for some outlets. 3) Future work is needed to extend the analysis to more sites, stories, and time periods to better understand patterns of news spread over time and during major events.

1. CRICOS No.00213J
Sharing, Spamming, Sockpuppeting:
Comparing the Twitter Dissemination Careers of News Articles
from Mainstream and Suspect News Outlets
Axel Bruns, Tim Graham, Brenda Moon, Tobias R. Keller, Dan Angus
a.bruns / timothy.graham / brenda.moon / tobias.keller / daniel.angus @ 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
• Seven of the most shared FakeNIX outlets: Gateway Pundit, Raw Story, Breitbart, Daily Caller, Daily Beast, Russia Today, Judicial Watch
• Timeframe:
• First tweet sharing article during 1-7 July 2019; subsequent tweets to 31 Aug. 2019
• Reach:
• ATNIX: any articles with at least 200 shares by 31 Aug. 2019 – 85 articles, ~43,000 tweets in total
• FakeNIX: any articles with at least 1000 shares by 31 Aug. 2019 – 201 articles, ~840,000 tweets in total

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Dissemination Careers
• Approach:
• For each individual story:
• t0: timestamp of the first tweet sharing the story URL (between 1 and 7 July 2019)
• tmax: timestamp of the final tweets sharing the story URL (on or before 31 Aug. 2019)
• 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
Average dissemination careers per site

6. under 10h to 50%
7-13h to 30%
1-2¼ days to 70%1+ weeks: late rise
⅓-½ month to 50%

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https://cran.r-project.org/package=growthcurver
Credit: Kathleen Sprouffske

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Co-Retweet Networks
• Definitions and objectives
• FAKENIX – 10,418 nodes and 42,913 edges
• Giant cluster of MAGA/Pro-Trump coordinated
amplification
• JudicialWatch (JW) sockpuppeting
• @EvangTwitBot and @ConservTwitBot
• Co-retweeted JW 249 times
• Both suspended
• By comparison, the ATNIX co-retweet network only
resulted in 48 nodes and 33 edges

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(Exploratory) URL Co-Tweet Analysis
• Non-retweet co-tweeting of URLs:
• Same URL posted by multiple accounts in (apparently) independent original tweets
• Threshold used: tweets within 60 seconds of each other
@account_a: Check out this story… http://abc.net.au/storyurl123 (2019-07-03 15:04:07)
@account_b: Well this is interesting: http://abc.net.au/storyurl123 (2019-07-03 15:04:38)
 co-retweet connection @account_b (later)  @account_a (earlier)
• Not inherently problematic, but suspicious when occurring repeatedly between same two accounts
• Repeated URL co-tweeting may indicate astroturfing

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Judicial Watch:
Trump ‘re-tweet’ accounts
The Conversation:
[City]Connect accounts
(now suspended)
Edges coloured by average time between URL co-tweets, from yellow (0 secs.) to red (60 secs.)

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Key Observations and Further Outlook
• Overall:
• Mis-/disinformation doesn’t necessarily disseminate faster than ‘real’ 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 strongly affected by (authentic or inauthentic) coordinated activities
• Evidence of artificial boosting through co-retweeting and co-tweeting
• Next steps:
• Current study limited to major stories from major sites first shared during 7 days in July 2019
• Plan to extend analysis to longer-term, larger-scale data and broader range of sites and stories
• 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 in part by the Australian Research Council projects
DP200101317 Evaluating the Challenge of ‘Fake News’ and Other
Malinformation, DP160101211 Journalism beyond the Crisis: Emerging
Forms, Practices, and Uses, 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.
Acknowledgments