Shipra Sanganeria
Social media has reached a level of ubiquity that new generations are now commonly referred to as the “social media generation.” Due to the popularity of these platforms — and the variety of topics that get tackled, dissected, and promoted in these spaces — content moderation has been essential.
Platforms claim to strictly enforce community guidelines, but to what extent are they willing or able to do so?
While regular users get flagged or shadowbanned (meaning content visibility is limited or restricted without official notice) for using words like “sex” or “kill,” news outlets can use these terms freely.
And with social media platforms’ recent shift to artificial intelligence (AI)-based moderation, users have learned to bypass restrictions with coded language like “seggs” or “unalive.”
We at SafetyDetectives are dedicated to helping create a safe and secure digital environment for everyone by providing accurate and valuable information on topics involving cybersecurity and privacy. As such, we wanted to find the answer to one important question: Are social media platforms really enforcing guidelines, or are they just creating an illusion of safety?
In this research, we compare the censorship and content moderation policies of major platforms and investigate whether they are actually effective or just security theater. We analyze which content gets censored the most, how users evade restrictions, and whether all accounts are treated equally.
How Strict Are Different Platforms?
To better understand how major platforms moderate content, we studied and compared the community guidelines of Meta, TikTok, YouTube, and X.
We must note that platforms’ guidelines often evolve, so the information used in this study is based only on the latest available data at the time of publication. Moreover, the strictness and regularity of policy implementation may vary per platform.
Content Moderation
We were able to categorize 3 main methods of content moderation in major platforms’ official policies: AI-based enforcement, human or staff review, and user reporting.
Notably, TikTok is the only platform that doesn’t officially employ all 3 content moderation methods. It only clearly defines the process of user reporting, although it mentions that it relies on a “combination of safety approaches.” Content may go through an automated review, especially those from accounts with previous violations, and human moderation when necessary.
Human or staff review and AI enforcement are observed in the other 3 platforms’ policies. In most cases, the platforms claim to employ the methods hand-in-hand. YouTube and X (formerly Twitter) describe using a combination of machine learning and human reviewers. Meta has a unique Oversight Board that manages more complicated cases.
All platform policies include the implementation of account bans for repeat or single “severe” violations. Of the 4 platforms, TikTok and X are the only ones to include circumventing moderation enforcement as additional grounds for account banning.
Content Restrictions
Content depicting graphic violence is the most widely prohibited in platforms’ policies, with only Meta allowing it with conditions (the content must be “newsworthy” or “professional”).
Adult content is also heavily moderated per the official community guidelines. X allows them given there are adequate labels, while other platforms restrict any content with nudity or sexual activity that isn’t for educational purposes.
YouTube is the only one to impose a blanket prohibition on gory or distressing materials. The other platforms allow such content but might add warnings for users.
All platforms have a zero-tolerance policy for content relating to child exploitation. Other types of potentially unlawful content — or those that threaten people’s lives or safety — are also restricted with varying levels of strictness. Meta allows discussions of crime for awareness or news but prohibits advocating for or coordinating harm.
Other official metrics for restriction include the following:
What Gets Censored the Most?
Overall, major platforms’ community and safety guidelines are generally strict and clear regarding what’s allowed or not. However, what content moderation looks like in practice may be very different.
We looked at censorship patterns for videos on major social media platforms, including Instagram Reels, TikTok, Facebook Reels, YouTube Shorts, and X.
The dataset considered a wide variety of videos, ranging from entertainment and comedy to news, opinion, and true crime. Across the board, the types of content we observed to be most commonly censored include:
- Profanity: Curse words were censored via audio muting, bleeping, or subtitle redaction.
- Explicit terms: Words pertaining to sexual activity or self-harm were omitted or replaced with alternative spellings.
- Violence and conflict: References to weapons, genocide, geopolitical conflicts, or historical violence resulted in muted audio, altered captions, or warning notices, especially on TikTok and Instagram.
- Sexual abuse: Content related to human trafficking and sexual abuse had significant censorship, often requiring users to alter spellings (e.g., “s3x abuse” or “trffcked”).
- Racial slurs: Some instances of censored racial slurs were found in rap music videos on TikTok and X.
Instagram seems to heavily censor explicit language, weapons, and sexual content, mostly through muting and subtitle redaction. Content depicting war, conflict, graphic deaths and injuries, or other potentially distressing materials often require users to click through a “graphic content” warning before being able to view the image or video.
Facebook primarily censors profanity and explicit terms through audio bleeping and subtitle removal. However, some news-related posts are able to retain full details.
On the other hand, TikTok uses audio censorship and alters captions. As such, many creators regularly use coded language when discussing sensitive topics. YouTube also employs similar filters, muting audio or blurring visuals extensively to hide profanity and explicit words or graphics. However, it still allows offensive words in some contexts (educational, scientific, etc.).
X combines a mix of redactions, visual blurring, and muted audio. Profanity and graphic violence are sometimes left uncensored, but sensitive content will typically get flagged or blurred, especially once reported by users.
News and Information Accounts
Our study confirmed that news outlets and credible informational accounts are sometimes subject to different moderation standards.
Posts on Instagram, YouTube, and X (from accounts like CNN or BBC) discussing war or political violence were only blurred and presented with an initial viewing warning, but they were not muted or altered in any way. Meanwhile, user-generated content discussing similar topics faced audio censorship.
On the other hand, comedic and entertainment posts still experienced strict regulations on profanity, even on news outlets. This suggests that humor and artistic contexts likely don’t exempt content from moderation, regardless of the type of account or creator.
The Coded Language Workaround
A widespread workaround for censorship is the use of coded language to bypass automatic moderation. Below are some of the most common ones we observed:
- “Fuck” → “fk,” “f@ck,” “fkin,” or a string of 4 special characters
- “Ass” → “a$$,” “a**,” or “ahh”
- “Gun” → “pew pew” or a hand gesture in lieu of saying the word
- “Genocide” → “g*nocide”
- “Sex” → “s3x,” “seggs,” or “s3ggs”
- “Trafficking” → “tr@fficking,” or “trffcked”
- “Kill” → “k-word”
- “Dead” → “unalive”
- “Suicide” → “s-word,” or “s**cide”
- “Porn” → “p0rn,” “corn,” or corn emoji
- “Lesbian” → “le$bian” or “le dollar bean”
- “Rape” → “r@pe,” “grape,” or grape emoji
This is the paradox of modern content moderation: how effective are “strict” guidelines when certain types of accounts are occasionally exempt from them and other users can exploit simple loopholes?
Since coded words are widely and easily understood, it suggests that AI-based censorship mainly filters out direct violations rather than stopping or removing sensitive discussions altogether.
Is Social Media Moderation Just Security Theater?
Overall, it’s clear that platform censorship for content moderation is enforced inconsistently.
Given that our researchers are also subject to the algorithmic biases of the platforms tested, and we’re unlikely to be able to interact with shadowbanned accounts, we can’t fully quantify or qualify the extent of restrictions that some users suffer for potentially showing inappropriate content.
However, we know that many creators are able to circumvent or avoid automated moderation. Certain types of accounts receive preferential treatment in terms of restrictions. Moreover, with social media apps’ heavy reliance on AI moderation, users are able to evade restrictions with the slightest modifications or substitutions.
Are Platforms Capable of Implementing Strict Blanket Restrictions on “Inappropriate” Content?
Especially with how most people rely on social media to engage with the world, it could be considered impractical or even ineffective to try and restrict sensitive conversations. This is particularly true when contexts are excluded, and restrictions focus solely on keywords, which is often the case for automated moderation.
Also, one might ponder whether content restrictions are primarily in place for liability protection instead of user safety — especially if platforms know about the limitations of AI-based moderation but continue to use it as their primary means of enforcing community guidelines.
Are Social Media Platforms Deliberately Performing Selective Moderation?
At the beginning of 2025, Meta made waves after it announced that it would be removing fact-checkers. Many suggested that this change was influenced by the seemingly new goodwill between its founder and CEO, Mark Zuckerberg, and United States President Donald Trump.
Double standards are also apparent in other platforms whose owners have clear political ties. Elon Musk, a popular supporter and backer of Trump, has been reported to spread misinformation about government spending — posting or reposting false claims on X, the platform he owns.
This is despite the platform’s guidelines clearly prohibiting “media that may result in widespread confusion on public issues, impact public safety, or cause serious harm.”
Given the seemingly one-sided implementation of policies on different social media sites, we believe individuals and organizations must practice careful scrutiny when consuming media or information on these platforms.
Community guidelines aren’t fail-safes for ensuring safe, uplifting, and constructive spaces online. We believe that what AI algorithms or fact-checkers consider safe shouldn’t be seen as the standard or universal truth. That is, not all restricted posts are automatically “harmful,” the same way not all retained posts are automatically true or reliable.
Ultimately, the goal of this study is to help digital marketers, social media professionals, journalists, and the general public learn more about the evolving mechanics of online expression. With the insights gathered from this research, we hope to spark conversation about the effectiveness and fairness of content moderation in the digital space.
Methodology
In conducting our study, we strived to ensure a broad and representative sample of censored content while capturing patterns in content moderation enforcement across platforms.
Data Collection Parameters
The dataset of videos was compiled using a combination of targeted hashtag searches and organic discovery on various social media platforms, including Instagram, TikTok, Facebook Reels, and YouTube Shorts.
We used hashtags to identify content related to sensitive topics, controversial discussions, and potential censorship cases. The hashtags were chosen to cover a diverse range of content that might be subject to content moderation, such as:
- #humantrafficking
- #truecrime
- #news
- #comedy
- #gaming
- #trending
- #storytelling
Our team also compiled a list of randomly suggested videos on different users’ personal feeds. This method ensured that the content included in our study was not solely influenced by targeted searches but also reflected real-world visibility on each platform.
Censorship Criteria and Data Points Analyzed
Each video was reviewed for specific types of content restrictions, with the following factors documented:
Limitations and Considerations
Algorithm Bias: Organic discovery was influenced by the recommendation systems of each platform, which meant that content visibility varied by user.
Shadowbanning Impact: Some censored content may have been deprioritized or hidden entirely by algorithms, making it difficult to analyze or even include in the study.
Incomplete Moderation Data: Since platforms do not always disclose why a video was restricted, the study relied on visible censorship markers like muted audio and blurred captions.
