Moderation is governance, and its authority is contested

Every platform that publishes what users write also decides what it will not publish, and the parties who make that decision are not chosen by the affected users. The eighth of ten units argues that moderation is a governance function whose problem is a deficit of legitimate authority rather than a shortage of rules, and that transparency, community norms and automation each change behaviour while leaving who may decide much as they found it (Grimmelmann, 2015; Gillespie, 2018). The next two units ask what such behaviour adds up to, and what can be known about it.

Moderation is governance, not its absence

Grimmelmann (2015) reaches the claim through a failure. In 2005 the Los Angeles Times opened a "wikitorial" that readers could edit, and vandals overran it within a weekend; the paper had copied Wikipedia's technology without the moderation that keeps open participation usable. His taxonomy's verbs are exclusion, pricing, organising and norm-setting, which he argues hold a community between the chaos of too much freedom and the sterility of too much control. The question is not whether to moderate but how, and by what right.

Fiesler and colleagues (2018) studied 100,000 subreddits and found communities writing and enforcing their own regulations, context-dependent for each yet sharing common characteristics across the site. Where so much is settled by rule, the standing of the rule-maker becomes the live question. Gillespie (2018) names why it is contested: platforms moderate constantly while presenting themselves as neutral conduits, so the decisions that shape who is heard are taken out of view.

The authority behind the rule is made elsewhere

If moderation is governance, the flag is its ballot paper, and Crawford and Gillespie (2016) show how narrow the paper is. The flag serves platforms twice over: as a way to curate an impossible volume of content, and as a rhetorical justification for removal. What can be complained about, and in what vocabulary, is fixed before any complaint is heard (Crawford and Gillespie, 2016). That vocabulary is written for a fluent reader, so a user with low literacy is heard only in the platform's terms.

Suzor and colleagues (2019) examine what the governed receive when a decision goes against them. From a thematic analysis of 380 survey responses they argue that meaningful transparency must be part of a communicative process of accountability to independent stakeholders, and propose that platforms say what content was moderated, which rule was breached, and who decided (Suzor et al., 2019). Transparency as generally supplied does not let a person contest a decision: the deficit is less secrecy than the absence of anyone to answer back to.

Governance runs on hidden labour

Roberts (2019) shows that the authority is exercised, in the first instance, by people who are themselves governed. Her ethnography of commercial moderation finds more than 100,000 moderators evaluating posts on mainstream platforms, enforcing internal policies, training automated systems and removing offensive material. They are, in her phrase, invisible by design, so their judgement decides what survives and is attributed to no one.

Seering and colleagues (2019) interviewed 56 volunteer moderators across three platforms and modelled the work as becoming a moderator, the tasks of moderating, and rule-making as a community develops (Seering et al., 2019). Wohn (2019) narrows the frame to 20 moderators of Twitch micro communities. She finds the work entangled in personal relationships with streamers and viewers, and the lines between emotional labour, physical labour and play hard to draw.

The tools those volunteers use are their own infrastructure. Cai and Wohn (2019) interviewed 21 Twitch moderators and sorted the real-time features they relied on into four functions, chat, content, viewer and settings control, drawn from the platform, third parties and tools the moderators built themselves.

Transparency changes behaviour, not authority

Jhaver and colleagues (2019) analysed 32 million Reddit posts to characterise the explanations offered when a post is removed, and modelled their link to later behaviour. Offering an explanation reduced the odds of a user's future removals, and human explanations had no significant advantage over bots (Jhaver et al., 2019).

An explanation that changes later conduct is an instrument of compliance; accountability requires that a person can challenge the decision and that an independent party can review it. Their system-level recommendation is access to large-scale disaggregated data, so that outsiders can examine moderation rather than take the platform's word for it (Suzor et al., 2019).

Since September 2023 the Digital Services Act has obliged the largest platforms to file a statement of reasons for every moderation action in the European Union. Trujillo, Fagni and Cresci (2025) analysed all 353.12 million records from the database's first 100 days and found the platforms adhered only in part to its structure, differed in what they reported, and left many records inconsistent. Disclosure on the scale Suzor and colleagues (2019) demanded now exists, and its quality is the finding.

Community norms are the best-evidenced remedy

One intervention here deserves the word effective: making norms visible. Matias (2019) randomised announcements of community rules across 2,190 discussions in a science community of 13 million subscribers. Displaying the rules raised newcomer rule compliance by more than 8 percentage points, and newcomer participation by 70 per cent on average.

A scale shock need not destroy a community either. Kiene and colleagues (2016) interviewed participants in Reddit's NoSleep community after a sudden, sustained influx of newcomers and found it survived without the collapse the Usenet "Eternal September" story predicts. Coordinated administrators, a shared sense of community and technical safeguards carried it.

Automation solves scale, not authority

Chandrasekharan and colleagues (2019) interviewed 11 moderators from 10 subreddits and built Crossmod, a classifier trained on cross-community data to assist Reddit moderators. Against conversations from two subreddits of over 10 million subscribers each, it detected the comments moderators would remove with 86 per cent accuracy, and four moderators said they would have removed 95.3 per cent of those flagged. 98.3 per cent of the flagged comments were still online, so the bottleneck is authority rather than detection.

Even if we could effectively automate content moderation, it is not clear that we should (Gillespie, 2020).

Gillespie (2020) argues that the turn to automated moderation is justified as a response to scale: the size of the large platforms is why artificial intelligence looks inevitable. His objection is not to accuracy but to the substitution of a computational judgement for a normative one. Chandrasekharan and colleagues (2017) tested Reddit's 2015 ban of r/fatpeoplehate and r/CoonTown across over 100 million posts and comments, and found those who stayed reduced their hate speech by at least 80 per cent. A mass expulsion worked, with no hearing for anyone.

Contested question: does enforcement repair the deficit, or only the behaviour?

The three positions measure different things. The first judges moderation by its effects: Chandrasekharan and colleagues (2017) find a ban that cut hate speech by at least 80 per cent, and Matias (2019) finds a rules announcement that raised newcomer compliance by more than 8 percentage points. Governance without a mandate is doing its job. The second holds that legitimacy is process: Suzor and colleagues (2019) show transparency as practised gives no means to contest a decision, Crawford and Gillespie (2016) show the vocabulary of complaint is fixed in advance, and Gillespie (2018) and Roberts (2019) locate the deficit in decisions taken out of view. The third treats automation as a solution to scale that sharpens the question: Gillespie (2020) argues it substitutes a computational judgement for a normative one, while Chandrasekharan and colleagues (2019) show detection is not the constraint.

The behavioural studies observe compliance and participation, as Jhaver and colleagues (2019) do; the process studies ask whether a person can answer a decision, which no behavioural study here records. What would settle it is a design that manipulates both: randomise whether a removal is explained and whether an appeal is offered, then measure later compliance alongside the person's judgement that the decision was legitimate, using the template Matias (2019) provides and the outcome Suzor and colleagues (2019) specify. No study cited here performs it.

Rate the three positions by what they measure. The effects position carries a randomised announcement across 2,190 discussions and a ban studied across more than 100 million posts (Matias, 2019; Chandrasekharan et al., 2017), alongside one observational study of 32 million posts (Jhaver et al., 2019). The legitimacy position now carries a disclosed moderation regime analysed across 353 million records (Trujillo et al., 2025). The scale position carries a classifier reaching 86 per cent accuracy (Chandrasekharan et al., 2019). While the dispute is open, design the explanation and the appeal, then record whether the person accepted the decision. The strongest objection is that compliance is the only measured outcome.

Where the argument is heading

The direction is toward treating moderation as a process to be designed rather than a volume to be cleared. Chandrasekharan and colleagues (2019) show moderation knowledge travelling between communities, while their own result shows tooling running ahead of the will to enforce. Gillespie (2020) separates what moderation can do from what it should do. Suzor and colleagues (2019) attach that argument to a demand for access to disaggregated data so moderation can be audited from outside, and Trujillo and colleagues (2025) show what the audit returns. The frontier is where governance by an external authority must justify itself to the governed.

The sources this unit rests on

Each note states what the document did and found. Identifiers were resolved on 2 November 2026.

• Chandrasekharan, E., Pavalanathan, U., Srinivasan, A., Glynn, A., Eisenstein, J. and Gilbert, E. (2017). You Can't Stay Here. doi:10.1145/3134666 Tested Reddit's 2015 ban of r/fatpeoplehate and r/CoonTown across over 100 million posts and comments. Hate speech fell by at least 80 per cent among those who stayed, with no displacement. Enforcement can succeed without accountability.
• Chandrasekharan, E., Gandhi, C., Mustelier, M. W. and Gilbert, E. (2019). Crossmod. doi:10.1145/3359276 Built a classifier trained on cross-community decisions after interviews with 11 moderators from 10 subreddits, reaching 86 per cent accuracy. Moderators would have removed 95.3 per cent of flagged comments, yet 98.3 per cent were still online. Automation solves detection, not enforcement.
• Crawford, K. and Gillespie, T. (2016). What is a flag for? doi:10.1177/1461444814543163 Argued that the flag serves platforms both as a curation tool and as a rhetorical justification, constraining what can be complained about and in what terms. The reporting affordance is itself a governance decision.
• Fiesler, C., Jiang, J. A., McCann, A., Frye, C. and Brubaker, J. R. (2018). Reddit Rules! doi:10.1609/icwsm.v12i1.15033 Analysed the rules of 100,000 subreddits, finding communities writing and enforcing their own regulations, context-dependent for each yet sharing common characteristics across the site. Written community governance is a mass practice.
• Gillespie, T. (2018). Custodians of the Internet. https://yalebooks.yale.edu/book/9780300261431/custodians-of-the-internet/ Examined how platforms police content while presenting themselves as neutral conduits, drawing on interviews with moderators, creators and users. Moderation shapes norms while receiving too little scrutiny, which is the legitimacy problem the unit turns on.
• Gillespie, T. (2020). Content moderation, AI, and the question of scale. doi:10.1177/2053951720943234 Argued that the push to automate moderation is justified by the demand to scale rather than by accuracy, and that it is not clear moderation should be automated even if it could be. It reframes the dispute as normative.
• Grimmelmann, J. (2015). The Virtues of Moderation. https://yjolt.org/virtues-moderation Argued from the failure of the Los Angeles Times wikitorial that moderation is a governance function, with a taxonomy whose verbs are exclusion, pricing, organising and norm-setting. It is the framework the governance thesis rests on.
• Jhaver, S., Bruckman, A. S. and Gilbert, E. (2019). Does Transparency in Moderation Really Matter? doi:10.1145/3359252 Analysed 32 million Reddit posts and modelled the effect of removal explanations, finding that explanations reduce the odds of a user's future removals and that bot explanations were not significantly worse than human ones. Transparency changes behaviour.
• Kiene, C., Monroy-Hernandez, A. and Hill, B. M. (2016). Surviving an "Eternal September". doi:10.1145/2858036.2858356 Interviewed members of Reddit's NoSleep community after a sudden influx of newcomers and found it survived without major incident, carried by coordinated administrators, a shared sense of community and technical safeguards. Communities can govern a scale shock.
• Matias, J. N. (2019). Preventing harassment and increasing group participation through social norms in 2,190 online science discussions. doi:10.1073/pnas.1813486116 Randomised rule announcements across 2,190 discussions in a community of 13 million subscribers. Displaying rules raised newcomer compliance by more than 8 percentage points and newcomer participation by 70 per cent on average.
• Roberts, S. T. (2019). Behind the Screen. https://yalebooks.yale.edu/book/9780300261479/behind-the-screen/ Ethnography of the commercial moderation industry, based on interviews from Silicon Valley to the Philippines, reporting that more than 100,000 moderators evaluate posts, invisible by design and under emotional toll. Governance is performed by an unseen workforce.
• Seering, J., Wang, T., Yoon, J. and Kaufman, G. F. (2019). Moderator engagement and community development in the age of algorithms. doi:10.1177/1461444818821316 Interviewed 56 volunteer moderators across Twitch, Reddit and Facebook and derived a three-process model of becoming a moderator, the tasks of moderating, and rule-making as community development. Moderators shape norms with and without algorithmic support.
• Suzor, N., Myers West, S., Quodling, A. and York, J. (2019). What do we mean when we talk about transparency? https://ijoc.org/index.php/ijoc/article/view/9736 Analysed 380 survey responses from people subject to moderation decisions, arguing that meaningful transparency must be accountability to independent stakeholders, with access to disaggregated data. It moves the remedy from disclosure to answerability.
• Wohn, D. Y. (2019). Volunteer Moderators in Twitch Micro Communities. doi:10.1145/3290605.3300390 Interviewed 20 moderators of Twitch micro communities and found their work bound up in personal relationships with streamers and viewers, with emotional labour, physical labour and play intertwined. Authority here rests on personal ties, not an office.
• Cai, J. and Wohn, D. Y. (2019). Categorising live streaming moderation tools. doi:10.4018/ijicst.2019070103 Interviewed 21 Twitch moderators and sorted the real-time moderation features they use into four functions, chat, content, viewer and settings control, drawn from platform, third-party and home-built tools. It shows moderation running on improvised worker-side kit.
• Trujillo, A., Fagni, T. and Cresci, S. (2025). The DSA Transparency Database: auditing self-reported moderation actions by social media. doi:10.1145/3711085 Analysed all 353.12 million statements of reasons filed by the eight largest platforms in the database's first 100 days, finding only partial adherence and many inconsistent records.