DigitalMANIPULATIVE

Astroturf Bots

What it is

Automated social media accounts that simulate grassroots opinion, amplify specific narratives, and create the illusion of popular consensus.

How it works

Networks of automated accounts post, like, share, and comment in coordinated patterns to make a position appear widely held. They can flood hashtags, drown out opposition, trend topics artificially, and create the impression that thousands of real people care about an issue that may have very limited genuine support.

Real-world examples

•State-sponsored bot networks generating millions of posts to influence elections in foreign countries.
•Corporate bot campaigns flooding social media with positive product mentions to drown out complaints.
•Political campaigns using bot networks to trend hashtags and create momentum narratives.

Historical case studies

Eighteen million fake comments on net neutrality

2017–2021US Federal Rulemaking

The Federal Communications Commission received about 22 million public comments on its 2017 proposal to repeal net-neutrality rules. The New York attorney general's investigation found that nearly 18 million were fake. The largest broadband companies had funded a roughly $4.2 million campaign through lead-generation firms that produced more than 8.5 million comments supporting repeal, using the names and addresses of real people without their consent. On the other side, a single 19-year-old college student used automated software to file more than 7.7 million comments supporting the rules under invented identities. Three of the firms paid penalties. The docket, meant to register public opinion, registered who had the better script.

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Devumi and the market in fake followers

2019FTC Enforcement

Devumi sold fake followers, subscribers, views and likes to actors, athletes, musicians, speakers and companies that wanted to look more influential on Twitter, YouTube, LinkedIn and other platforms, filling more than 58,000 orders for Twitter followers alone. Many of the accounts were bots, some built with the names and photographs of real people. The Federal Trade Commission's 2019 settlement, with a $2.5 million judgment against the owner, was its first case treating the sale of fake indicators of social media influence as a deceptive practice in itself, on the ground that buyers used the numbers to mislead potential clients, employers and customers.

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Ethical guidelines

●Simulating human opinion through automation is fundamentally deceptive.
●Bot networks corrupt the information commons that democratic societies depend on.
●Platforms have a responsibility to detect and remove inauthentic automated accounts.

How to defend against it

►Check account age, posting frequency, and follower patterns — bots often have tells.
►Be skeptical of topics that trend suddenly with suspiciously uniform messaging.
►Use tools like Botometer to check whether accounts engaging with you are likely automated.

From the Defense Playbook

Account Forensicsminutes

Before taking an online account's word, or taking a crowd of accounts as public opinion, spend a minute on the profile itself: its age, history, posting rhythm, network, and photo, which together show whether you are looking at a person, a persona, or a coordinated operation.

Sunlight Against Astroturftakes practice

When a "grassroots" campaign, citizens' coalition, or flood of public comments appears, establish who organized and paid for it and make that public, because manufactured support loses most of its force once the audience knows it is manufactured.

TARES Self-Auditminutes

Before a campaign, advertisement, fundraising appeal, or pitch goes out, test it against the five TARES duties: Truthfulness of the message, Authenticity of the persuader, Respect for the audience, Equity of the appeal, and Social responsibility for the common good.

The Front-Page Testseconds

Ask whether you would be comfortable seeing the tactic, including how it works and why you chose it, described accurately on the front page of a newspaper read by your audience; if the tactic only works when the audience does not know about it, treat that as a finding.

Harm-Class Inventoryminutes

List every persuasive element in a campaign, tag each one as neutral craft, dual-use, or manipulative by design, and for each dual-use element write down where the line is and which side of it you are on.

Disclosure Checklistminutes

Before publishing sponsored, affiliated, incentivized, or endorsed content, check that every material connection between the speaker and the brand or cause is disclosed clearly, conspicuously, in plain language, and in the same place and format as the claim it qualifies.

Every playbook entry states how strong its evidence is and when not to use it. Browse the full playbook.

DISARM framework

How analysts would file this in the open DISARM framework (v1.6.1, CC BY-SA 4.0). Closest matches, mapped by Persuasion Lab.

T0146.007Automated Account AssetT0049.003Bots Amplify via Automated Forwarding and RepostingT0093.002Acquire Botnets

References

Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96–104 · link
Survey of social bots: automated accounts that post, like and share in coordination to inflate apparent support, trend topics and drown out opposition.
Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2018). The spread of low-credibility content by social bots. Nature Communications, 9, 4787
Bots amplified low-credibility content early and targeted influential humans, disproportionately shaping what appeared popular.
U.S. Senate Select Committee on Intelligence (2019). Report on Russian Active Measures Campaigns and Interference in the 2016 U.S. Election, Volume 2: Russia's Use of Social Media. 116th Congress
Bipartisan documentation of the Internet Research Agency's automated and persona accounts used to influence a foreign election — the entry's first example.

Last reviewed 2026-09-12

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