Social Media News Spread Approaches to Misinformation
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Paper presented by Axel Bruns as part of the workshop Integrity 2021: Integrity in Social Networks and Media at the 14th ACM Conference on Web Search and Data Mining (WSDM) in Jerusalem, Israel, March 2021.
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Social Media News Spread Approaches to Misinformation
- 1. CRICOS No.00213J Social Media and the News: Approaches to the Spread of (Mis)information Axel Bruns (on behalf of the QUT Digital Media Research Centre team) a.bruns@qut.edu.au / @snurb_dot_info
- 4. CRICOS No.00213J News and Information
- 5. CRICOS No.00213J Axel Bruns and Tobias R. Keller. “News Diffusion on Twitter: Comparing the Dissemination Careers for Mainstream and Marginal News .” Paper presented at the Social Media & Society 2020 conference, online, 22 July 2020. News Diffusion
- 6. 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
- 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
- 10. CRICOS No.00213J ATNIX sites mainly in midfield
- 11. CRICOS No.00213J Raw Story, Gateway Pundit, and some Russian state media sites in Turkish, Spanish, Arabic disseminate most quickly
- 12. CRICOS No.00213J Specialist sites shared more slowly: The Conversation – scholarly contributions Judicial Watch – hyperpartisan lawfare
- 13. 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 • 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
- 15. CRICOS No.00213J Axel Bruns, Tim Graham, Brenda Moon, Tobias R. Keller, and Dan Angus. “Sharing, Spamming, Sockpuppeting: Comparing the Twitter Dissemination Careers of News Articles from Mainstream and Suspect News Outlets.” Paper presented at the International Communication Association conference, online, 20 May 2020. Marian-Andrei Rizoiu, Timothy Graham, Rui Zhang, Yifei Zhang, Robert Ackland, and Lexing Xie. #DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 US Presidential Debate. In 12th International AAAI Conference on Web and Social Media, June 2018. https://www.aaai.org/ocs/index.php/ICWSM/ICWSM18/pa per/view/17886 Timothy Graham, Robert Ackland, and Lewis Mitchell. A Novel Network-Based Approach to Detecting and Analysing Coordinated Inauthentic Behaviour on Twitter. Paper presented at the 5th annual Australian Social Network Analysis Conference (ASNAC2020), 25-27 November 2020, Perth. Inauthentic Behaviour
- 16. CRICOS No.00213J Co-(re)tweet Networks • Co-(re)tweeting: • Multiple accounts tweeting/retweeting same URL within seconds • Increasingly suspicious if behaviour is repeated • 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 Co-retweet network
- 17. CRICOS No.00213J 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.)
- 18. Debate Night 2016 • Dataset of 6.5 million tweets; 1.45 million accounts • See: Rizoiu, Graham et al. (2018) • Nodes: Twitter accounts. • Edges: Accounts that co-retweeted within 1 second of each other, at least twice. • 25,242 nodes • 89,708 edges • Account status colour codes: • Suspended • Deleted • Active
- 19. Debate Nights 2020 • Nodes: Twitter accounts. • Edges: Accounts that co-retweeted within 60 seconds of each other, at least twice. • Network filtered by minimum edge weight = 5. • 132,207 nodes (100% visible) • 1,759,336 edges (1.98% visible)
- 20. CRICOS No.00213J ‘Coordination network toolkit’ 0.1.0 • A small command line tool and set of functions for studying multiple types of coordination networks in Twitter and other social media data • Available now! Open source on GitHub and through PyPi • https://github.com/QUT-Digital-Observatory/coordination- network-toolkit • https://pypi.org/project/coordination-network-toolkit/
- 21. CRICOS No.00213J Axel Bruns, Stephen Harrington, and Eddy Hurcombe. "‘Corona? 5G? Or Both?’: The Dynamics of COVID-19/5G Conspiracy Theories on Facebook." Media International Australia (2020). DOI:10.1177/1329878X20946113. Tim Graham and Axel Bruns. “'Like a Virus' – Disinformation in the Age of COVID-19.” Seminar presented in the Australia Institute's Australia at Home series, 23 Apr. 2020. Conspiracy Theories
- 23. CRICOS No.00213J (Source: data provided by CrowdTangle) (Source: data provided by CrowdTangle)
- 24. CRICOS No.00213J (Source: data provided by CrowdTangle)
- 25. CRICOS No.00213J Key Takeaways So Far • Social media and the COVID-19 infodemic: • Conspiracy emergence parallels virus outbreak since mid-January 2020 • This seeds subsequent activity (especially on the right fringe) • Some evidence of coordinated inauthentic behaviour on both Facebook and Twitter • Notable content take-downs (on YouTube, Facebook, and Twitter), but much remains • Conspiracy theories and the media: • Substantial spread begins only once mainstream media amplify conspiracies • Entertainment and tabloid media serve as amplifiers for audiences beyond the conspiratorial fringe • Celebrity and politician endorsements provide further extension and amplification • Fringe media outlets report mainstream coverage as endorsement of their earlier stories • Official government and corporate statements arrive too late to counteract spread
- 27. CRICOS No.00213J Ariadna Matamoros-Fernández, Louisa Bartolo, and Betsy Alpert. Non-Problematic Uses of Automation: Examining the Sharing of Youtube Videos on Twitter at Scale and over Time. #SMARTdatasprint 2021: The Current State of Platformisation, Lisbon, 1-5 Feb. 2021. https://smart.inovamedialab.org/2021- platformisation/theme/ YouTube (Video soon.)
- 28. CRICOS No.00213J Data • Tweets matching (coronavirus OR wuhan) (youtube OR youtu.be) • 1 February to 31 May 2020 • Total number of unique tweets containing YouTube URLs: 1,716,203 • Unique YouTube video IDs: 830,058 • Data collected by the QUT Digital Observatory
- 29. CRICOS No.00213J BIN 0 Videos in this bin have only been tweeted once or twice per user on average “Potential viral videos” BIN 1 Videos in this bin have only been tweeted by a few to many times per user on average “Allegedly normally distributed bin” BIN 2 Videos in this bin have been tweeted a very high number of times per user on average “Suspicious automated behaviour”
- 30. RankFlow diagram of YouTube sharing on Twitter February 2020: Top 20 daily most shared YouTube URLs 2. The colour indicates whether many diverse or just a few unique users shared the same video 1. The height of the bar indicates the number of times the video was tweeted BIN 2: Suspicious behaviour BIN 0: Potential viral videos BIN 1: Bin to be explored
- 31. CRICOS No.00213J Observations • Users do not only engage in automated behaviour for ideological purposes (e.g. ‘foreign interference’) or commercial purposes (e.g. spammers), but also as a professionalisation tactic • Some of these strategies fit into platforms’ understanding of ‘inauthentic behaviour’, but this behaviour is often not coordinated nor linked to problematic content • Inauthentic or automated sharing of YouTube videos around newsworthy events on Twitter (e.g. COVID-19) is not always “coordinated” • Platforms’ content-agnostic approach to “platform manipulation” can have unintended consequences for aspiring content creators eager to gain visibility at all costs • In many cases, it seems that the only way to make a distinction between deceptive and non- deceptive behaviour is to focus on content
- 32. CRICOS No.00213J Nicholas Carah and Daniel Angus. Algorithmic Brand Culture: Participatory Labour, Machine Learning and Branding on Social Media. Media, Culture & Society 40.2 (2018): 178–194. https://doi.org/10.1177/0163443718754648 Instagram
- 33. CRICOS No.00213J Insta-explorer • Designed to aid qualitative analysis of Instagram content • Accepts json and image data gathered via Instamancer • https://github.com/andyepx/insta-explorer/releases
- 34. CRICOS No.00213J The Image Machine
- 35. CRICOS No.00213J
- 38. CRICOS No.00213J Scraping (Is Not a Dirty Word) • Post-API age (Freelon, 2018) • Server-side API for research would be great, but are we going to die waiting? (Bruns, 2019) • Pragmatic response is to improve access and utility of scraping tools: • Technical advances, e.g. grafting • Make code available, and documented
- 40. CRICOS No.00213J Platforms and Researchers • Unresolved tensions: • Social media platforms are too important to be left unscrutinised • Critical, independent, public-interest research is crucial • Shaping of research limits by platform directives is unacceptable • Terms of Service cannot trump public interest • Commitment by the research community: • Critical self-assessment of our uses of data • Diligent adherence to established ethical standards • Careful management of data and sharing practices • Great care to demonstrate that we can be trusted with the data • Commitment by platform providers?
- 41. CRICOS No.00213J Cato the Elder, ca. 157 BCE Ceterum censeo Carthaginem esse delendam. Furthermore, I propose that Carthage is to be destroyed.
- 42. CRICOS No.00213J Social Media Researchers, ca. 2021 CE Furthermore, we demand that social media platforms provide data access to critical, independent, public-interest research.
- 43. CRICOS No.00213J This research is funded by the Australian Research Council projects DP200101317 Evaluating the Challenge of ‘Fake News’ and Other Malinformation, DP200100519 Using Machine Vision to Explore Instagram’s Everyday Promotional Cultures, and FT130100703 Understanding Intermedia Information Flows in the Australian Online Public Sphere, by the Department of Defence Science and Technology (DST Group) and the Operations Research Network (ORnet), by the Australia Institute’s Centre for Responsible Technology, and by the Swiss National Science Foundation postdoc mobility grant P2ZHP1_184082 Political Social Bots in the Australian Twittersphere. It is also supported by the Australian Research Council Centre of Excellence for Automated Decision-Making and Society. Computational resources and services used in this work were provided by the QUT eResearch Office, Division of Research and Innovation. Facebook data are provided courtesy of CrowdTangle. Coordination Network Toolkit developed with Sam Hames and Betsy Alpert from the QUT Digital Observatory. Acknowledgments