Out of all forms of content moderation, text seems like it would be the easiest to handle. Unfortunately, rule breakers always invent new ways to sidestep filters and blocklists, which means organizations must consistently refine them to account for these harmful innovations.
For example, one user types a slur with numbers instead of letters, or another posts self-harm hints without using any banned words.
Sometimes, efforts to crack down on these violations result in compliant users getting hit with warnings or bans due to the limits of automated systems.
In this guide, we’ll explore the top keywords to block in 2026. We’ll also cover where you can find premade lists, how to use them, and the role LLMs play in improving your moderation efforts.
What Is Keyword Filtering?
Keyword filtering is a content moderation technique that scans text for specific words, phrases, or patterns. It then triggers a predefined action when it detects them.
These actions can include flagging content for further review, limiting visibility, or blocking publication altogether.
At its core, keyword filtering relies on curated lists, called keyword lists or blocklists, that represent terms associated with policy violations or elevated risk.
Keyword filtering can be applied across many types of user-generated content (UGC), including:
- Social media posts and comments, where short-form text is published at high volume
- Live chat messages, such as in communities, gaming platforms, or customer support tools
- Forum posts and discussion threads, which may contain longer and more nuanced language
- Usernames, display names, and bios, to prevent abusive or misleading identifiers
- Direct messages (DMs), depending on a platform’s safety and privacy model
- User-submitted content, such as reviews, captions, or form responses
Top Keyword Lists Used in Moderation
Here are some popular resources that collect examples and reference lists you can use to guide moderation decisions:
- List of Ethnic Slurs: Wikipedia’s reference for hate speech based on ethnicity. They also have other lists, including a category page with links to lists for gender- and sex-based slurs.
- GitHub Blacklisted Words Master List: 600+ entries covering hate speech, violence, and other categories.
- English Profanity List: A public repository of commonly used bad words in English that can be used for basic filtering in JSON and plain text formats.
- Hate Speech Dataset Catalogue: A catalogue of datasets and keyword lists related to hate speech and online abuse in several languages.
- Hatebase: A widely used but no longer maintained database of hate speech terms across languages.
Pros and Cons of Keyword Filtering
Keyword filtering works best when teams understand both what it does well and where it falls short.
Pros
- Fast to implement: Teams can get a basic keyword filter running quickly without in-depth training or complex infrastructure.
- Easy to automate and maintain: Moderators can add or remove terms without breaking the system. It makes iteration simple when new words, slurs, or risk-phrases appear.
- Great as a first safety net: Keyword lists help catch obvious high-risk content early, before it reaches users or requires deeper, context-based review.
- Good for narrow, well-defined risks: Some terms, such as explicit slurs or minor-related language, can safely trigger immediate action with little ambiguity.
Cons
- Easy to evade: Users can bypass filters using leetspeak, spacing, emojis, or creative spellings that basic keyword matching fails to catch.
- Doesn’t catch nuance or intent: Keyword filters can’t detect sarcasm, polite threats, or harmful statements that avoid explicit terms.
- Needs constant updates: Language changes fast. New slang, coded language, and trends mean lists need regular review to avoid blind spots.
- Accidental overmoderation: Strict blocklists can flag neutral conversations around identity, activism, and mental health, which limits accessibility and inclusion.
- Language limitations: Keyword lists often work best in dominant languages and struggle with regional dialects, mixed languages, or transliterated speech. This makes it harder to catch harmful content in multilingual communities
Top Keywords to Block by Category
Rather than relying on a single master list, moderation teams can group keywords by risk category. This allows platforms to apply different thresholds, actions, and review paths depending on the type of harm involved.
Below are common keyword categories, along with illustrative examples that show how these terms often appear in real UGC.
Hate Speech
Hate speech keywords include words that target people based on characteristics such as race, religion, ethnicity, nationality, sexual orientation, gender identity, or disability.
Example patterns and terms include:
- Racial, religious, or other slurs, including altered spellings and substitutions, such as “nzi”
- Dehumanizing phrases such as “go back to where you came from” or “your kind shouldn’t exist”
- Coded slogans used in extremist or fringe communities, such as dog whistles or phrases like “replace them” or “pure blood” that signal hostility without naming a group directly
- Direct insults like “loser” or “pathetic”
Self-Harm and Violence
These keywords are used to identify content where someone may be at risk of harming themselves or others.
The language can be direct, but it often appears in softer or indirect forms that still suggest intent or distress.
Self-harm examples include:
- Direct statements, such as “I want to kill myself” or “I’m going to hurt myself”
- Method-related phrases like “cut myself,” “overdose,” or “jump off”
- Indirect expressions, such as “I don’t want to exist anymore” or “everyone would be better without me”
Violence-related examples include:
- Threats like “I’m going to hurt them” or “they deserve to die”
- Statements describing planned harm, such as “I’m bringing a weapon” or “I’ll make them pay”
- Language that celebrates or encourages physical harm
Sexual or Explicit Content
Sexual and explicit keywords are used to identify text that contains sexual language, descriptions, or solicitation that may violate platform rules, age restrictions, or local laws.
Examples include:
- Explicit anatomical references or graphic sexual acts, described in direct or slang-based terms
- Solicitation phrases such as “DM for pics” or “link in bio 🔞”
- Common abbreviations and emojis used to signal adult content, including terms like “NSFW,” “18+,” or emojis (such as 🍑 and 🍆)
Spam and Scam Terms
Spam and scam keywords are used to detect messages meant to mislead users, trick them into taking action, or push unwanted promotions at scale.
The language in scams changes often, as bad actors adjust their wording to bypass filters.
Examples include:
- Financial bait, such as “guaranteed returns,” “easy money,” or “get rich fast,” which promise rewards that are unlikely or impossible
- Urgent calls to action (like “act now” or “limited time”) designed to pressure users into responding quickly
- Impersonation or phishing phrases, such as “official support,” “account suspended,” or “security alert,” which pretend to come from a trusted company or authority
Radicalization and Terrorism Terms
Keywords in this category relate to extremist ideologies, terrorist organizations, or calls for political violence. They may appear in propaganda, recruitment attempts, or glorification of past attacks.
Examples include:
- Names of known extremist groups or leaders
- Slogans, chants, or acronyms associated with violent movements
- Language encouraging violence, martyrdom, or calls to take up weapons
Because these terms can also appear in journalistic or academic discussions, contextual analysis is critical.
Drugs and Illicit Trade
This category includes keywords associated with illegal substances and prohibited goods.
Examples include:
- Slang terms for drugs like “xans,” “perk,” or “molly”
- Transactional phrases, such as “for sale,” “ships discreetly,” or “DM to buy”
- Emoji-based signals commonly used in illicit trade
How to Find Premade Keyword Lists
Premade keyword lists give moderation teams a practical starting point. Instead of building everything from scratch, these lists reflect patterns that other platforms, researchers, and safety organizations have already identified.
They help teams move faster and avoid overlooking known forms of harmful language, especially in high-risk areas like hate speech, self-harm, and exploitation.
Below are common sources for building and maintaining keyword lists.
Public Repositories
Open-source repositories like GitHub and ML dataset platforms like Kaggle are some of the most accessible ways to find premade keyword lists and moderation resources.
Developers, researchers, and trust and safety practitioners often publish repositories containing blocklists, regex patterns, or labeled examples of abusive, spammy, or harmful language.
Teams can search directly on GitHub using terms like “content moderation keywords”, “hate speech lexicon”, “spam blocklist”, or “abusive language dataset.”
Many repositories include README files that explain how the list was created, what types of content it covers, and when it was last updated.
Trust & Safety Organizations and Research Groups
Groups like the Center for Countering Digital Hate (CCDH) or the Berkman Klein Center for Internet & Society publish research, datasets, and reports on online harm.
While they may not always provide ready-made blocklists, their work helps define what harmful content looks like in practice and offers informed guidance on which terms, patterns, or narratives deserve closer monitoring.
Government and Nonprofit Databases
Some government-backed and nonprofit organizations maintain databases focused on specific harm categories.
For example, the Internet Watch Foundation (IWF) works to identify and remove child sexual abuse material (CSAM), providing paid members with a keyword list of terms linked to offenders.
Internal Platform Data
Over time, your own platform can become one of the most valuable sources for keyword development.
By logging flagged UGC, reviewing moderation queues, and tracking repeated violations, teams can identify recurring words, phrases, and evasive patterns. These insights can then be fed into an internal keyword lexicon that reflects the realities of your specific community and product.
How to Use a Keyword List
Premade keyword lists are easy to explore and adapt, but the quality varies widely. Lists may be outdated or designed for a different platform, region, or content moderation policy.
To use them effectively, here are some suggestions:
1. Start With the List as a Baseline
A premade list shows the types of language that commonly cause harm, but it won’t necessarily reflect your platform’s audience, features, or norms.
For example, a list built for harmful social media content may not work as-is for private chats, usernames, or other types of activity feeds.
Before applying any rules, teams should scan the list to remove irrelevant terms and flag high-risk categories, such as self-harm or extremist language, that may require human review rather than automation.
2. Normalize Before Importing
Raw keyword lists often include duplicates, inconsistent casing, or formatting that can reduce accuracy. Normalization ensures the list behaves predictably once it’s live.
This typically includes:
- Converting all terms to lowercase
- Removing extra spaces or punctuation
- Standardizing plural forms and common variants
- Separating single words from multi-word phrases
For example, grouping related word forms like “scam,” “scams,” and “scamming” helps ensure the same rule applies no matter how the word appears. Without this step, one version might be flagged while another passes through unnoticed.
3. Decide What Action Each Keyword Triggers
Not every keyword should trigger the same response. Some terms may justify blocking content outright, while others should only flag content for review.
For instance:
- Explicit spam phrases might trigger automatic removal
- Self-harm language may route content to human moderators or safety workflows
- Ambiguous terms may only reduce visibility or generate a warning
Mapping keywords to actions helps avoid overmoderation and ensures responses match the level of risk.
In many cases, slowing the interaction works better than stopping it outright with a direct action. One common approach is a self-correction nudge.
When a user types a phrase that appears toxic or risky, the system shows a short prompt such as: “This message may violate our community standards. Do you want to edit it before posting?”
This moment of friction gives users a chance to pause, rethink, and rephrase.
4. Add Pattern Matching
Exact keyword matches are easy to bypass. Pattern matching helps detect common evasion tactics, such as misspellings, spacing tricks, or character substitutions.
Examples include:
- Detecting variations like “sc@m” or “s c a m”
- Catching numeric substitutions such as “1” for “i”
- Identifying repeated phrases used across spam messages
5. Review and Update Regularly
As mentioned earlier, a downside of keyword lists is their need to constantly be updated to account for language changes, new methods of circumvention, and correcting overmoderation.
Regular review should be tied to real signals, such as:
- Terms that generate high false positives
- New words appearing repeatedly in moderation queues
- Emerging trends flagged by moderators or community reports
For instance, if the word “kill” triggers alerts frequently even in positive contexts, like “you killed that presentation,” your team can lower the sensitivity of the keyword or require additional context before taking action.
How LLMs Improve Keyword Filtering
Keyword filtering is effective at catching known risk words, but it often misses meaning. Large language models (LLMs) strengthen automated moderation by analyzing full sentences, tone, and intent instead of reacting to single keywords.
For example, in a fintech app’s community forum, a message like “Oh, you’re clearly a financial genius” to someone who just lost money may pass a basic keyword filter. An LLM can recognize the sarcastic tone and flag it as harassment, even though no abusive words appear.
LLMs also go beyond earlier AI moderation systems built with simpler natural language processing (NLP) capabilities. While NLP-only systems typically rely on predefined rules, sentiment scores, or keyword proximity, LLMs evaluate language more holistically. They can recognize counterspeech, veiled threats, or emotional escalation by understanding how ideas connect across a message.
In practice, keyword filters and LLMs work best together. Keywords quickly surface potentially risky content at scale. The LLM then reviews those cases, interprets user intent, and helps prevent harmless posts from being removed or reported unnecessarily. This layered approach adds judgment where rigid rules fall short, without slowing moderation workflows.
Best Practices for Keyword-Based Content Moderation
Here are some best practices that’ll help you build and maintain keyword lists to ensure UGC quality and safety.
Build Lists That Match the Risk Profile of Your Product
Different products face different risks. A gaming chat app may need stronger controls for harassment and threats, while a dating app may focus more on sexual content and scams.
Start by listing the behaviors you want to prevent and build keyword groups around those risks instead of relying on an unedited, premade blocklist.
Localize Blocklists
Language varies by region, dialect, and culture. A keyword that is harmless in one country may be offensive in another. Where possible, maintain separate lists by language to enable multilingual moderation and validate them with native speakers.
If resources are limited, prioritize languages by UGC volume or risk level rather than trying to cover everything at once.
Combine Blocklists With User Reports and Moderation Review
Modern keyword filtering works best when three things support each other: automated filters, user reports, and hybrid moderation.
Keyword lists catch obvious violations quickly, but they still miss a lot, especially on larger platforms.
This is where users can help. Someone in the community notices the post or message, understands what it really means, and reports it. That report adds context that the filter alone can’t see.
An LLM-based system reviews the report and takes action based on its settings, weighing context and tone.
For the trickiest cases, a human moderator steps in. They can then update the list of keywords based on their findings. This way, any new tricks can be blocked automatically the next time. They can also step in to handle malicious reports that are targeting harmless UGC.
This feedback loop helps moderation systems improve over time instead of staying static.
Track Metrics Like False Positives and False Negatives
Tracking mistakes helps keep filters useful. For example, teams might monitor how often flagged content turns out to be safe, or how often harmful posts slip through without being flagged.
These signals help refine rules and prevent moderation from drifting away from policy goals.
Frequently Asked Questions
- What Keywords Should I Block for Parental Control?
This will vary significantly by age. A keyword list for a 7-year-old will be much more restrictive than one for a 16-year-old.
A list designed for younger users often prioritizes blocking grooming language, such as “are your parents home?” or “send me a pic.”
For older teens, filters usually focus on higher-risk areas like self-harm, drugs, or gambling, with terms like “thinspo,” “xans,” or “crypto casino.”
Age-aware filtering helps reduce real harm without blocking normal, age-appropriate content.
- What Is a Keyword Blocklist?
A keyword blocklist is a set of words, phrases, or patterns that trigger moderation actions when detected in text.
- How Do Blocklists Work?
Blocklists scan text for predefined keywords or patterns. When a match appears, the system applies a rule, such as blocking the message, flagging it for review, or routing it to a moderation queue.
- What Is an Example of Content Moderation?
An example of content moderation is automatically flagging a post containing potential hate speech, then having a human moderator review the context and decide whether to remove or allow it.
- What Are the Ethical Issues in Moderation?
Ethical issues include overblocking harmless speech, bias against certain groups or dialects, lack of transparency in enforcement, and the mental health impact on human moderators reviewing harmful content.
Conclusion
Keyword filtering helps keep online spaces safe, but it works best as part of a broader moderation strategy.
On its own, it can catch clear-cut violations, but it needs support from more sophisticated automation, human review, clear policies, and ongoing feedback to be effective.
As platforms grow, the way people communicate and misuse language also changes. Moderation tools and rules need to evolve with that growth. When teams regularly adapt their safety systems, they reduce harm while allowing normal conversation to continue.