Astroturfing

Astroturfing is the deceptive practice of hiding the sponsors of an orchestrated message or organization (e.g., political, economic, advertising, religious, or public relations) to make it appear as though it originates from, and is supported by, unsolicited grassroots participants.^[1] It is a practice intended to give the statements or organizations credibility by withholding information about the source's financial backers.

The implication behind the use of the term is that instead of a "true" or "natural" grassroots effort behind the activity in question, there is a "fake" or "artificial" appearance of support. It is increasingly recognized as a problem in social media, e-commerce, and politics. Astroturfing can influence public opinion by flooding platforms like political blogs, news sites, and review websites with manipulated content. Some groups accused of astroturfing argue that they are legitimately helping citizen activists to make their voices heard.

While the term “astroturfing” often evokes images of corporate lobbying or political media manipulation, its function as a mechanism for manufacturing consent transcends liberal democracies. In their foundational work Manufacturing Consent, Edward S. Herman and Noam Chomsky argue that power is reproduced not merely through censorship but through the orchestration of discourse, where the appearance of grassroots consensus is shaped by elite interests. This dynamic plays out in authoritarian contexts like China, where the state has adopted astroturfing as a strategic tool to manage, rather than suppress, online expression. As Rongbin Han documents in Manufacturing Consent in Cyberspace: China's “Fifty-Cent Army”, the Chinese government recruits and trains anonymous online commentators to seed pro-regime narratives across forums and comment sections, presenting them as spontaneous public sentiment. Far from simply muzzling dissent, this practice reflects a sophisticated state effort to simulate legitimacy and manage perception within digital public spheres. Yet ironically, as Han's research shows, these efforts often fail due to poor coordination, lackluster incentives, and the lingering bureaucratic logic of top-down propaganda, ultimately undermining the very trust they aim to build.^[2]

Many countries have laws prohibiting some astroturfing practices with various methods of enforcement. In the US, the FTC has set rules against endorsing a product without disclosing that one is paid to do so.^[3] In the EU, social networking sites may be governed by the Unfair Commercial Practices Directive which also prohibits undisclosed paid endorsements and connected individuals from misleading readers into thinking they are regular consumers.^[4]

Various detection methods have been developed by researchers, including content analysis, linguistic analysis, authorship attribution, and machine learning.^[5]

While these approaches have been instrumental in flagging inauthentic behavior, such as bot-like posting patterns or coordinated message drops, more recent scholarship emphasizes that astroturf detection also requires interpretive analysis of messaging strategies. Brieuc Lits (2020), in his study of pro-shale gas lobbying campaigns, argues that astroturfing often succeeds not simply by masking sponsorship but by adopting discursive frames that mimic those of authentic civic groups, through what Lits terms “corporate ventriloquism,” private interests assume the voice of the public, strategically emphasizing values like economic freedom or energy independence to mask underlying industrial agendas. Lits claims that these language choices are not accidental; they are calculated to evoke grassroots legitimacy while marginalizing competing narratives, such as those centered on environmental harm or community health. As a result, detection now involves more than just identifying false identities or automation; it demands scrutiny of how language, symbols, and values are mobilized to simulate authenticity.^[6]

In addition to content and linguistic cues, coordination-based detection methods have gained traction as a means of identifying astroturfing campaigns. Schoch et al. (2022) propose a scalable, network-based approach that focuses on identifying synchronized patterns of behavior, such as co-tweeting or co-retweeting identical messages within short time windows, as indicators of centralized coordination. This method, rooted in principal-agent theory, assumes that hired agents or campaign employees tend to “shirk,” reusing content and showing repetitive, time-bounded activity (e.g., during office hours). By mapping message coordination networks, the study was able to reliably distinguish astroturfing accounts from organic grassroots actors across dozens of global campaigns. Unlike bot-centric detection, this strategy targets behavioral traces unique to organized disinformation and has proven robust even when automated behavior is minimal or absent.^[7]