Computational propaganda
Propaganda that depends on software: automated accounts, algorithmic amplification, targeted advertising, and the data behind them, used to manipulate public opinion at a scale and speed no human operation could manage. The term from the Oxford Internet Institute's research program of the mid-2010s.
Philip Howard and Samuel Woolley defined computational propaganda as the use of algorithms, automation, and human curation to purposefully distribute misleading information over social networks, and their project at Oxford tracked it across dozens of countries from 2016. The emphasis on computation is the point. Older propaganda needed printing presses and transmitters and the staff to run them; computational propaganda needs code, and the code can run thousands of voices from one desk.
Its components are the Digital-era family on this site. Bot Amplification makes a claim look popular. Sock Puppets and Synthetic Personas give it faces. Microtargeting delivers different versions to different audiences, so that no one sees the contradiction. Algorithmic Laundering uses the platform's own recommendation system to carry a claim from the fringe to the mainstream. Engagement Bait and Rage Farming exploit the ranking signals that platforms reward.
The platforms are not neutral ground. Their systems were built to maximize attention, and attention is what propaganda has always sought; computational propaganda is in part the discovery that the two goals coincide. Countermeasures are therefore partly technical, in detection and account removal, and partly a matter of changing what the systems reward.
Compare information operations and astroturf.
Sources
- Woolley, Samuel C., and Philip N. Howard, eds. Computational Propaganda: Political Parties, Politicians, and Political Manipulation on Social Media. New York: Oxford University Press, 2018. ISBN 978-0190931407
- Howard, Philip N. Lie Machines: How to Save Democracy from Troll Armies, Deceitful Robots, Junk News Operations, and Political Operatives. New Haven: Yale University Press, 2020. ISBN 978-0300250206