Shadowbanning

The term shadowbanning first appeared in 2001, where it referred to the mechanism of removing posts for everyone else except for the poster in an online forum (Savolainen 2022). It reached broader public awareness in 2018 when US conservatives began accusing Twitter (now X) of shadow-banning ‘prominent Republicans’ by not suggesting them on the platform’s autofill drop-down search bar (Savolainen 2022; Stack 2018). Shadowbanning trades under many different names such as stealth banning, ghost banning, hell banning, comment ghosting, visibility moderation, (visibility) reduction, suppression, borderline content policies, and uninformed or undisclosed content moderation (Gillespie 2022a, 2022b; Jaidka et al. 2021; Nicholas 2023). Shadowbanning suppresses the visibility or reach of content, users, or groups without alerting the affected party (Gillespie 2022a). Platforms limit the conditions under which the content circulates, for example, whether it appears as a recommendation, a search result, in a news feed and users’ queries, or in a stream of comments (Gillespie 2022b). It is an opaque mechanism where content remains theoretically accessible and visible to (some) users (e.g., the poster themselves) (Savolainen 2022). It is important to emphasize that today’s understanding of shadowbanning by demoting people, groups, or content with algorithmic support is much more nuanced than the original 2001 mechanism of simply withholding content from anyone (Gillespie 2022b).

Four main types of shadowbans (see Table 1) can be identified (Jaidka et al. 2023): (1) Ghost bans are the most restrictive type of shadowbans that obfuscate content by allowing only the accountholders themselves to see their own content (Jaidka et al. 2023). (2) Search bans prohibit the intended encounter with users or content by removing them from the platforms’ internal search index (e.g., quarantined subreddits from Reddit’s internal search, false posts from Instagram’s hashtag search) (Goldman 2021). (3) Search suggestion bans remove content from the platform’s internal search engine’s “auto-suggest” feature (Goldman 2021) so that the account does not appear when others look it up in the search interface (Jaidka et al. 2023). (4) Downtiering deprioritizes content to limit the unintentional exposure and to make it unlikely for other users to find it by hiding replies under an interstitial and only loading when prompted (Jaidka et al. 2021, 2023; Nicholas 2022; Ryan et al. 2020), downgrading the internal search visibility, reducing internal promotion (e.g., recommendations), and reducing or removing navigation links (e.g., explore pages) (Goldman 2021).

Shadowbanning differs from the standard content curation mechanisms employed by social media platforms through recommender algorithms. Content recommendation follows a different set of concerns and priorities (Gillespie 2022a). Recommender systems and newsfeeds select for what is deemed most appealing to optimize for engagement measured by the time spent on the platform, the types of actions taken, and satisfaction proxies (Bucher 2018; McKelvey and Hunt 2019). They collect and analyze user data together with the corpus of all available content to personalize user feeds and maximize user engagement. Content that is more positive – e.g., in terms of recency, serendipity, close tie popularity – has greater likelihood to be selected by recommender algorithms. Shadowbans are not simply an outcome or byproduct of recommender algorithms because shadowbanned content is actively suppressed or obscured, even in search results (Jaidka et al. 2023). Shadowbanning aims to select out what is deemed least appealing based on negative signals that indicate an item ought not to be recommended (Gillespie 2022a). The gold standard for Trust & Safety performance is ‘prevalence’ and shadowbanning aims to minimize the function of how often users view certain content (Fishman and Harris 2023).

We broadly understand shadowbanning as social media platforms’ use of algorithms to align user behavior, accounts, and content with organizational objectives. This conceptualization closely resembles the prevalent definition of algorithmic control (Wiener et al. 2023), being embedded into a broader organizational context (Alizadeh et al. 2023) with human controllers (Cram and Wiener 2020) and organizational intentions (Kellogg et al. 2020; Sullivan et al. 2024), fulfilling sanctioning functions – among others – (Hirsch et al. 2023), complementing and relieving but not entirely replacing human actors (Wiener et al. 2023). Current research on algorithmic control functions (Alizadeh et al. 2023; Hirsch et al. 2023; Kellogg et al. 2020; Sullivan et al. 2024) is yet to consider visibility reduction techniques from platforms like Uber (Uber 2023). Shadowbanning’s implicit form of algorithmic control delivery by avoiding to alert users also appears to be an extreme form of the previously investigated algorithmic opacity and limited transparency of algorithms (Möhlmannn et al. 2023). Given that algorithmic management researchers recognize the relation of algorithmic control to broader organizational contexts beyond work settings (Cameron et al. 2023), we draw on the related algorithmic management and control literature (Benlian et al. 2022) to guide our conceptualization of shadowbanning in the following (Fig. 1).

In the context of shadowbanning, the human controllers are Trust & Safety teams who define and communicate organizational intentions in the form of platform policies or policy violations, help implement policies into content moderation algorithms, manage or conduct content moderation themselves, and deal with content moderation appeals (Fishman 2023). Shadowbans are typically automated (e.g., to reduce visibility for spam bot posts) but still allow human moderators to enforce shadowbans directly (e.g., to occult certain types of users) (Biddle et al. 2020; Duffy and Meisner 2022). Algorithmic content moderation automates the human Trust & Safety teams moderation tasks (He et al. 2024) in order to gain scalability and consistency in content moderation (Jiang et al. 2023). For shadowbanning this involves the algorithmic detection of unwanted content or users based on Trust & Safety policies and enactment of respective ghost-, search-, search suggestion ban, or downtiering. Shadowbanning solutions can be developed by platforms themselves or sourced from external entities (e.g., Reddit’s AutoModerator) (Wright 2022).

Shadowbanning is part of social media platforms broader content moderation efforts to contain the prevalent online issues which means ‘governance mechanisms that structure participation in a community to facilitate cooperation and prevent abuse’ (Grimmelmann 2015, p. 47). While content moderation mechanisms are hypothetically unlimited from a technological standpoint, research has identified a range of common mechanisms (Goldman 2021) (Table 2). These content moderation mechanisms vary (1) in their degree of severity and (2) regarding their underlying philosophy.

The degree of severity is commonly broken up into either “hard” or “soft” types of content moderation (Zannettou 2021). Hard moderation means to suspend, block or remove content or entities from social media platforms (Gorwa et al. 2020). Soft forms of content moderation warn about content or contain its impact without suspension or take-downs (Jaidka et al. 2023). Content moderation mechanisms’ underlying philosophies differ in the degree to which they rely on nurturing or punishing to create a positive online environment (Jiang et al. 2023). Punishing is a reactive approach that primarily focuses on applying consequences for rule-violating behaviors. Nurturing predominantly intends to educate, improve or reform online (mis)behavior. In the following, we apply these dimensions to integrate the different forms of shadowbans into the broader context of other content moderation mechanisms as identified and described by Goldman (2021) (Table 2).

From a philosophical standpoint, shadowbanning is particularly opaque compared to nurturing mechanisms. Users are usually not informed that (or for how long) they are shadowbanned or need to retrieve the information themselves when possible (Silva 2022). Indeed, a major reason for using shadowbans is to disallow malicious actors (e.g., spam bots) to learn from and adjust to content moderation algorithms (Biddle et al. 2020). This prevents users from learning from the shadowbanning decisions and is thus a more punitive measure rather than one that nurtures user behavior in line with community standards.

The different forms of shadowbans vary in their degree of severity (Jaidka et al. 2023). Shadowbans can include both hard and soft types of content moderation depending on the degree to which they enforce visibility restrictions (i.e., do not recommend at all/ as much/ to some) (Gillespie 2022a). A ghost ban matches an account suspension without notifying the user (Goldman 2021). Similarly, shadowbans that severely restrict visibility in situations with a short “lifespan” of engagement equate to a suspension (Jaidka et al. 2023) (e.g., removal from the internal search index). Instead of making content inaccessible, shadowbans can also greatly conceal items to make it less likely that users may find them (Jaidka et al. 2021; Nicholas 2022; Ryan et al. 2020) (e.g., downgrade internal search visibility, no auto-suggest in the search function, no or reduced internal promotion, no or reduced exposure in navigation links such as “most popular” or “newly available”). Overall, we consider shadowbanning to be a predominantly punitive mechanism with its specific forms differing in their degree of severity between hard and soft forms of content moderation.

The debate around the prevalence and impact of shadowbanning is highly politicized. On one side, social media platforms are reluctant to admit to shadowbanning (Gillespie 2022a), often framing it as a specific kind of behavior (e.g., “it doesn’t hide people’s content for posting too many hashtags”) (Cotter 2021, p. 1234) or referencing its aforementioned original meaning in internet forums (Gillespie 2022b; Savolainen 2022). The absence of a unified, industry-accepted definition for shadowbanning (Gillespie 2022a) complicates discussions and fuels misunderstandings (Elmimouni et al. 2024). Platforms are understandably wary of being scrutinized for their policies (either for being interventionist and biased, or opaque and unaccountable) and aim to avoid the politicization of their content moderation practices (Gillespie 2022a).

From the platforms’ perspective, shadowbanning serves as an effective tool to manage problematic content. First, it helps mitigate “lawful but awful” content. This content that trades under different names across platforms (e.g., borderline, sensitive, harmful, undesirable, or objectionable content) (Cotter 2021) is considered detrimental to the user experience, threatens the health of the community, is misleading or salacious. It fails to violate platform policies (Gillespie 2022b) and therefore evades other forms of content removal. Examples include pro-eating disorder), sexually suggestive, firearm, and implicitly drug-related content (Gillespie 2022a) or misinformation (Gillespie 2022a). Second, shadowbanning allows platforms to hide moderation tactics from malicious actors, such as spammers (e.g., clickbait, links to malicious or deceptive sites) (Cotter 2021), bots (e.g., astroturfing, sockpuppeting), or orchestrated disinformation campaigns, preventing them from adjusting their strategies (Llewellyn et al. 2019; Nicholas 2022). Third, it provides a means to avoid public outcries over “censorship” while maintaining flexible moderation, particularly in response to evolving threats like increasing radicalization (Gillespie 2022a).

On the other side, the lack of transparency around shadowbanning has fueled folk theories and speculation among users, policymakers, and watchdogs (Jaidka et al. 2023) (e.g., Elon Musk uses shadowbanning on X to suppress Tesla’s employee union account (Masnick 2023)). Reports, especially from marginalized communities, suggest shadowbanning is used to suppress certain voices (Elmimouni et al. 2024). User surveys and interviews on shadowbanning are often discarded through “black box gaslighting” by asserting that users do not have sufficient understanding of content curation algorithms to determine whether they were shadowbanned (Cotter 2021). Platforms explain media reports on shadowbanning as technical glitches, the users’ failure to create engaging content, or as a matter of chance through the platform’s black-box algorithms (Cotter 2021). This back-and-forth erodes public trust in social media platforms and contributes to societal polarization (Chen and Zaman 2024; Jaidka et al. 2023).

The scrutiny surrounding shadowbanning also stems from its potential for misuse. Cases have emerged where shadowbanning was allegedly used to meet government demands (e.g., silence dissent in China, contain Covid-19 messages in the USA) (AP 2024; Hern 2019), marginalize minority voices (e.g., handicapped, black, or LGBTQ + users) (Delmonaco et al. 2024; Duffy and Meisner 2022), or manipulate public opinion (e.g., mute pro-Palestinian posts) (Chen and Zaman 2024; Elmimouni et al. 2024; Luu 2023). This form of exclusion can result in emotional distress (Lutz and Schneider 2021), financial harm for content creators (Duffy and Meisner 2022), and increased vulnerability to online attacks (for an overview, see Delmonaco et al. 2024; Nicholas 2022). For marginalized groups, shadowbanning can lead to their exclusion from public discourse or bans by association (Delmonaco et al. 2024; Elmimouni et al. 2024). On a broader societal level, the secrecy and lack of recourse foster distrust and conspiracy theories, while entrenching societal divides (Chen and Zaman 2024; Nicholas 2022).

The opaque nature of shadowbanning has inspired considerable work on establishing the prevalence of shadowbanning. A major source of evidence are social media platforms themselves when reporting content moderation mechanisms that impose a form of visibility reduction while avoiding the term “shadowbanning” (Merrer et al. 2021). Reddit is the only platform that openly confirms the traditional form of shadowbans (Nicholas 2022). Elon Musk promised to provide more transparency on shadowbanning after taking over Twitter (now X), which is yet to be delivered (Perez 2023) and instead faces scrutiny himself over shadowbans on posts with links to other social media platforms (Newton 2023). Meta’s Facebook and Instagram were the first major social media platforms that declared pursuing ways to algorithmically reduce user engagement with borderline content in May 2018 (Gillespie 2022a; Zuckerberg 2021). YouTube, X, LinkedIn, and TikTok have since disclosed applying similar strategies to dealing with sensitive content (Duffy and Meisner 2022) and Uber reports visibility reductions for restaurants in response to unfavorable customer reviews (Uber 2023) (for more details on platform statements on shadowbanning, see Delmonaco et al. 2024; Gillespie 2022a).

Beyond official social media platform statements concerning visibility reduction mechanisms, additional evidence suggests the existence and prevalence of shadowbanning. Anecdotal evidence comes from trace ethnography, surveys or interviews (Nicholas 2022; Wright 2022), analyses of platforms’ content moderation patents (Nicholas 2023), and users who closely monitor their engagement statistics (e.g., content creators) (Duffy and Meisner 2022; Zeng and Kaye 2022). Other evidence comes from sources that are difficult to verify such as internal whistle-blowers (Chen 2019), information leaks (Gillespie 2022a), or investigative journalism (Colve 2018; Merlan 2020). To determine the extent of shadowbanning, a recent survey of 1,006 social media users found that 9.2% report having been shadowbanned. Of these 8.1% were on Facebook, 4.1% on Twitter (now X), 3.8% on Instagram, 3.2% on TikTok, 1.3% on Discord, 1% on Tumblr, and less than 1% on YouTube, Twitch, Reddit, NextDoor, Pinterest, Snapchat and LinkedIn (Nicholas 2022). A content moderation survey that oversampled marginalized identities (i.e., racial and ethnic minorities, LGBTQ + people, trans and/or nonbinary people) even found that 21.78% of respondents reported shadowbanning (Delmonaco et al. 2024).

Recently, researchers have begun collecting statistical evidence for the presence and extent of different forms of shadowbanning. Analyses of 41 k to over 2.5 million Twitter (now X) accounts found that between 3–6.2% of accounts had been shadowbanned at least once (Jaidka et al. 2021; Merrer et al. 2021). These studies identify characteristics that increase the likelihood of shadowbans. Some of these predictors are particularly new accounts (less than two weeks old) with low follower numbers (below 200), using incivility (negative or offensive terms) or posting pictures without text messages, and displaying bot-like behavior (high botometer score) (Jaidka et al. 2023; Merrer et al. 2021). A verified account (e.g., the blue checkmark on X) helps to drastically reduce the chance of a shadowban (Jaidka et al. 2023; Jorgenson 2022). Based on these findings, computer scientists were then able to develop tools or workarounds that identify whether accounts are shadowbanned for different platforms like X (hisubway,¹ yuzurisa²) or Reddit (r/CommentRemovalChecker, r/ShadowBan) (Nicholas 2022).

While there is currently little anyone can do if their account or content is shadowbanned, researchers identified common strategies that creators use to prevent and cope with shadowbans: Suppression, experimentation, circumvention, and resignation (Duffy and Meisner 2022). Similarly, creators’ experiences helped to develop techniques for individual users to detect whether they were shadowbanned (Columbres 2023). However, user reports of shadowbanning frequently encounter “black box gaslighting,” a phenomenon where platforms dismiss these concerns by claiming users misunderstand the complexities of content moderation algorithms (Cotter 2021). IS research can explore methods for users to safely communicate potential shadowbanning experiences (1.1)? Similarly, shadowbanning has significant implications for the targets (Myers West 2018; Nicholas 2022). Core Self-Evaluation theory (Bono and Judge 2003; Judge et al. 2003), for example, can help us understand (and mitigate) the shadowbanning effects on users’ self-esteem, self-efficacy, locus of control, and emotional stability (1.2). Lastly, shadowbanning has considerable monetization ramifications for creators (Duffy and Meisner 2022; Myers West 2018). Self-determination theory (Ryan and Deci 2000) allows to examine the complexities of the algorithmically driven workplace at the individual level (Benlian et al. 2022). Here it can help us understand how shadowbanning affects content creators or regular users’ (e.g., job candidate) perceived competence, autonomy, and relatedness after being shadowbanned (1.3). Shadowbanning also poses a major challenge for marginalized groups that appear to be disproportionally targeted (e.g., “ugly, poor, or disabled” users, “Black Lives Matter” filter, LGBTQ + keywords) (Duffy and Meisner 2022; Ryan et al. 2020; Walsh 2022). The DIME model (Louis et al. 2020) allows IS scholars to explore the responses by these movements to shadowbanning (1.4).

Shadowbanning helps platforms fulfil important societal tasks by moderating various forms of harmful content. While its secrecy is strongly contested (e.g., promote conspiracies, reduce trust) (Nicholas 2022), shadowbanning’s opaque nature helps to avoid politicizing content moderation (Gillespie 2022a). The broader implications of content moderation mechanisms are controversially discussed among law and policy scholars (e.g., Douek (2022; Goldman (2021); Gorwa et al. (2020)), in communications research (e.g., Suzor et al. (2019)), and computer scientists [e.g., Seering (2020), Jhaver et al. (2019a), Jhaver et al. (2019b)]. IS research should join the debate and explore shadowbanning’s ability to mitigate societal or group polarization (Sunstein 2018) or protect free speech (Riemer and Peter 2021) compared to other forms of content moderation (2.1, 2.2). Alternatively, research demonstrates that a verified account and blue checkmarks are protective factors against shadowbanning (Jaidka et al. 2023; Jorgenson 2022), which are now partly offered for sale (Klar 2023). IS researchers could address this issue, for example, by applying the critical theory of power and ethics by Foucault (2007) to explore whether shadowbanning is an ethical, political, or primarily a technical problem (Siapera and Viejo-Otero 2021). Researchers could also aim to delineate the intended versus unintended consequences of systematic biases within shadowbanning (2.3).

Moderating problematic social media content is a major concern for advertisers that want to avoid having their ads displayed next to hateful content (Cooban 2023). While shadowbanning confines the visibility of problematic content, it remains potentially accessible online. IS scholars can apply, for example, brand safety (Bishop 2021) or situational crisis communication theory concepts (Coombs 2007) to assess advertisers’ concerns regarding platforms shadowbanning (2.4). We highlighted the heated politicized debate around the term shadowbanning (Cotter 2021; Gillespie 2022a). The good reasons for performing shadowbans are often met with harsh criticism of censorship and marginalization. Researchers could follow other examples of IS research on contested online behaviors (e.g., doxing) (Franz and Thatcher 2023) to determine boundary conditions for ethically acceptable shadowbanning (2.5) and whether some platforms’ efforts that allow users to trace their account status means meaningful improvements (2.6) (Silva 2022; Zakharchenko 2024). In this regard, while we have summarized the evidence on the prevalence of shadowbanning on social media platforms, other platforms (e.g., Gig-economy) also report forms of visibility reduction (Uber 2023). Shadowbanning offers research the opportunity to expand their conceptualization of algorithmic control functions (Alizadeh et al. 2023; Hirsch et al. 2023; Kellogg et al. 2020; Sullivan et al. 2024), refine algorithmic management to benefit society at large (Möhlmannn et al. 2023), and explore the prevalence of shadowbanning on other (non-social media) platforms (2.7).

Shadowbanning is primarily implemented through content moderation algorithms. These algorithms automate the large-scale enforcement of human-designed Trust & Safety policies (He et al. 2024; Jiang et al. 2023). Reliably detecting shadowbanning remains a key challenge due to its opaque nature and the platform’s unwillingness to share deeper insights into their algorithms. It requires identifying normal or expectable levels of engagement given the characteristics of the user (e.g., number of followers) and their content (e.g., topic, hashtags, links) (Gillespie 2022b; Nicholas 2022). Some detection tools and methods exist (e.g., hisubway, yuzurisa, r/CommentRemovalChecker) (Jaidka et al. 2021; Merrer et al. 2021), however, they frequently defunct (e.g., formerly Treiberr, shadowban, whosban) (Nicholas 2022) and are limited to select platforms. IS scholars could leverage, for example, new APIs for Meta (Ryan-Mosley 2023) or TikTok (TikTok 2024) and publicly available datasets (SOMA 2024) to develop new shadowbanning detection tools (3.1). Given the prevalence of lawful but awful content across internet platforms such as Spotify (Lima and Schaffer 2022) and Amazon (Bogle 2022; Dreisbach 2021), IS researchers could thereby contribute valuable insights into across platforms shadowbanning differences (e.g., Uber, Deliveroo, Craigslist). In a similar vein, the pervasive issues of algorithmic biases against vulnerable user groups (Feuerriegel et al. 2020; Spiekermann et al. 2022) and lack of transparent, explainable, or interpretable algorithms (Kim and Routledge 2018) also affects algorithmically enforced shadowbanning decisions (Cotter 2021). Prior research has established, for example, that verified accounts and higher engagement numbers are a protective factor against shadowbanning (Jaidka et al. 2023). Biases that reward celebrity status, audience size, and the ability to pay for account verifications challenge the notion of social media platforms as means for crowd empowerment (Leong et al. 2019). IS scholar could use the aforementioned resources to refine the detection algorithms and help debias and elucidate shadowbanning decisions or follow the existing precedent interview and case-studies (Delmonaco et al. 2024; Elmimouni et al. 2024) to better understand algorithmic bias in the context of shadowbanning (3.2).

The algorithmic management and control literature (Benlian et al. 2022; Cameron et al. 2023) proposes users’ algorithm aversion against automatically enforced decisions (Berger et al. 2021; Spiekermann et al. 2022). Targets of shadowbanning loose trust into platforms and regulators giving rise to conspiracy theories (Nicholas 2022). Targets of shadowbans report considering leaving the platforms or withdrawing from social media altogether (Delmonaco et al. 2024; Elmimouni et al. 2024). IS scholars could build on the respective algorithm control and sensemaking literature (Möhlmann 2021; Möhlmann et al. 2021; Möhlmannn et al. 2023), for example, to design shadowbanning moderation mechanisms that are perceived as less threatening. This would also contribute insights into the general role of algorithms on trust building and the individual’s attitudes towards algorithmic control (Kizilcec 2016; Lee 2018) and designing control algorithms in a way that benefits platforms and society at large (Cameron et al. 2023) (3.3). Similarly, the emergent literature on algorithmic audiencing recognizes the role of algorithmic content moderation and distribution for free speech (Riemer and Peter 2021). This new, extended understanding of free speech in social media could be complemented by a greater consideration of the reductionist effects of shadowbanning through recommender systems – as opposed to the predominant focus on content virality and amplification (3.4) (Milli et al. 2023). Lastly, while shadowbanning is predominantly algorithmically-enforced, it has been found to be enacted by humans. IS researchers could engage with Trust & Safety teams to learn more about the differences between manual and automated shadowbanning processes and provide much needed insights into the process of content moderation more broadly (3.5) (Gorwa et al. 2020; Grimmelmann 2015). Such research could lead to more dedicated research on the different forms of shadowbanning (i.e., ghost, search, search suggestion, downtiering) (Jaidka et al. 2023) in terms of their efficacy, ramifications, and fields of application. We believe a more nuanced understanding of the different forms of shadowbans is essential to inform public discourse (3.6).