Text Moderation

User-generated content (UGC) is everywhere, making moderation essential to keep digital spaces welcoming.

Text moderation is one example, and it's crucial for keeping community discussions and comment sections from becoming chaotic and potentially harmful.

What Is Text Moderation? 

Text moderation is the process of reviewing and analyzing user-generated text to manage and prevent the spread of harmful, inappropriate, or otherwise unwanted content.

Whether on social media or in the comment section, moderation is important for:

• Users to feel safe from abuse and harassment

• Platforms to foster environments free of hostility and conflict, so that users keep returning

• Brands to preserve their image and reputation, and to avoid potential legal issues

How Does Text Moderation Work? 

Text moderation starts by identifying harmful content for your platform. Then, using artificial intelligence (AI) and machine learning (ML) along with human judgment, it decides whether UGC should be allowed or removed. 

Defining Moderation Guidelines

The first step is to define clear content policies and rules that outline what you consider inappropriate.

Remember that "inappropriate" is a relative term. For instance, a brand that sells products to young adults might be fine with some slang or even profanity, while a professional networking event host wouldn't.

Some harmful content types that are usually filtered are: 

• Adult content that can involve descriptions of sexual acts or nudity.

• Hate speech, which could be prejudicial or abusive speech, slurs, or bullying based on race, ethnicity, religion, gender, sexuality, or ability.

• Violence and gore, which can involve descriptions of graphic violence and excessive gore, threats of harm, abuse, torture, or bloodshed.

• Spam and scam content, where there is repetitive, unsolicited, or deceptive text often used to manipulate users, such as phishing attempts, fraudulent schemes, misleading advertisements, or fake giveaways.

• Brand defamation involves statements damaging a brand's reputation, values, or image. It may also include promoting a competitor's content maliciously.

• Personal data sharing includes revealing sensitive information, such as addresses, phone numbers, login credentials, financial details, and other personally identifiable information (PII).

Submitting Content 

This process is quite straightforward. Users may post comments, reviews, or other content via forums, apps, or websites.

A user writes, "This product is terrible and so are the people who buy it" on an online store. This comment can now be moderated in two ways, which the platform can decide. It can choose to take action either:

• Before publishing, or 

• After publishing.

The latter approach is known as reactive moderation. 

Deciding Who Moderates What

There are also different types of moderation. The content can be moderated by: 

• Human content moderators: A dedicated team of moderators analyzes and filters messages and comments.

• AI content moderation: Automated systems scan for hateful chats and messages with pre-designed filters.

• A mix of both (Hybrid): Along with AI moderation, human judgment is needed.   

• Users or community: Other users in the community vote for the content to be removed or report it for mods or AI to address.

Using Text Moderation Techniques 

Platforms use multi-layered methods to find and handle potentially unsafe content. From basic filters to advanced machine learning, each layer adds more context and understanding to help catch subtleties and nuance.

Surface-Level Techniques

This layer applies basic techniques and rule-based methods to quickly filter harmful content, such as:

• Regular Expressions

• Filters

• Blocklists

Regular Expressions (RegEx)

RegEx is a set of pattern-matching rules that helps catch banned words or formats. Here, you must define every fragment, word, or phrase you want to match. It's fast but very literal, and their syntax varies by programming language.

Suppose you want to flag the word idiot even if it's in different cases (Idiot, IDIOT, iDiOt, and so on).

So, here's a simple RegEx you may use: (?i)\bidiot\b

What this does:

(?i) → makes the match case-insensitive

\b → matches only whole words, not substrings

idiot → the word we want to detect

It'll flag "You are an idiot" and "IDIOT!" but will ignore "idiotic behavior" since it's not the exact word.

RegEx can quickly detect highly specific patterns, such as email addresses, URLs, numbers, or exact words and phrases, but it doesn't understand meaning. 

Filters 

Filters act as first-line defenses in text moderation. Different types of filters detect specific patterns, behaviors, or other types of harmful UGC.   

• Harm Detection Filters: Analyze text to identify unsafe content types like hate speech, self-harm, and sexual content across multiple languages and cultural contexts.

• Semantic Filters: Detect variations of harmful language in paraphrased or coded speech that would evade basic keyword detection. For instance, semantic filters can recognize "ur a cl0wn" as a variation of "you are a clown."

• Blocklists: Flag any mentions of words, phrases, or patterns that are strictly prohibited on the platform.

• Pattern Recognition Filters: Detect spammy or repetitive behavior patterns, such as mass advertising links or "buy now" phrases.

• Language Detection and Localization: Identify language, regional dialects, slang terms, metaphors, and idioms to apply culturally appropriate moderation rules.

• Image and Text (Multimodal Moderation): Uses Optical Character Recognition (OCR) to extract and analyze text embedded in images (like memes with offensive or derogatory captions).

Mid-Level Techniques

Mid-level techniques go beyond basic keyword matching to improve detection by analyzing language tone and structure. Primary methods include:

• Natural Language Processing (NLP) 

• Categorization

• Confidence scores 

Natural Language Processing (NLP) 

NLP helps machines understand meaning in human languages. For example:

Multiple NLP techniques are used in text moderation:

Tokenization is the process of breaking text into smaller units (called tokens). Tokens can be words, parts of words, or even characters. AI systems use tokens to read and analyze text. 

For example, breaking "You are a clown" into smaller parts so the system can flag it as an insult.

Keyword extraction is pulling out keywords like "hideous" in a body-shaming comment, "This outfit makes you look hideous and unfit."

Instead of analyzing the whole text, the system zooms in on the most harmful words.

Sentiment analysis determines if the text is positive, neutral, or negative. A statement like "You are brilliant!" would be considered positive.

• Example text: "You are brilliant!"

• Sentiment Analysis: Positive tone, direct praise

• NLP Interpretation: Genuine, sincere language

• Classification: Positive

But, in this statement "Wow, you're such a genius, lol", sentiment analysis will miss the mocking tone and term this as positive because of the word "genius". 

• Example text: "Wow, you're such a genius, lol"

• Sentiment Analysis: Positive (due to the word "genius")

• NLP Interpretation: Mocking tone, sarcastic use of genius

• Classification: Negative (likely personal attack)

Name Entity Recognition (NER) detects names, places, organizations, and other personal details. 

In the sentence "Meet me at Starbucks on 5th Avenue, John", NER will tag John as a person, Starbucks as an organization, and 5th Avenue as a location. This can help prevent doxxing and the sharing of personal information. 

Part-of-Speech (PoS) Tagging labels each word's role: noun, pronoun, verb, adjective, adverb, preposition, articles, and so on. So, in "You are a clown.", the PoS tags would be

• You → Pronoun

• are → Verb

• a → Article

• clown → Noun

This method is useful because sometimes insults only make sense in a grammatical context:

• "Clown" as a noun → insult.

• "Clown around" as a phrasal verb → harmless joke. 

Categorization 

Categorization labels text into predefined categories such as hate speech, profanity, threats, or personal information.

Confidence scores 

Moderation systems assign a percentage-based score to all UGC that shows the likelihood that the assigned classification is correct.

Lower thresholds flag more borderline posts, and higher thresholds catch only the most obvious violations. The right balance depends on your community's tolerance and goals.

• Confidence Level: ≥ 80%

  • Likelihood of Harm: Strong

  • Typical Action: Auto-remove or immediate action

• Confidence Level: 60--79%

  • Likelihood of Harm: Possible

  • Typical Action: Flag for review or cautious removal

• Confidence Level: < 60%

  • Likelihood of Harm: Unclear or unlikely

  • Typical Action: Log, ignore, or monitor for patterns

A professional network might act on anything above 60%, keeping the environment strict and safe. A social platform may only remove UGC flagged above 90%, allowing more room for debate and expression.

Advanced-Level ML and LLM Techniques

This most advanced layer uses ML models and LLMs to interpret user intent, wider context, and more subtle nuances in written text. 

Machine Learning Models and LLMs

Early models such as Naïve Bayes and support vector machines (SVMs) were used to detect spam and identify offensive keywords. These models were perfect for straightforward patterns, but they struggled with context and evolving language.

This is why large language models (LLMs) were developed. Built on the foundations of machine learning, LLMs can process entire sentences or even conversation history instead of single words or phrases. This allows them to:

• Recognize intent

• Catch subtle forms of hate speech

• Adapt to new slang 

• Pick up on cultural references that traditional ML models may miss.

Let's take this sentence again: "Wow, you're such a genius... can't believe you messed that up again". LLM-Based Harm Detection will read the entire sentence and understand that "genius" is used sarcastically. Its likely outcome would be flagging this statement as an insult, depending on the platform's harm rules. 

How These Techniques Can Work Together

Text is first scanned by RegEx and blocklists to remove overtly prohibited content immediately. NLP breaks down the text, understands sentiment, and extracts meaningful keywords.

ML and LLMs analyze deeper context, intent, and harmful cues, offering confidence scores for moderation decisions. Content is then categorized and routed to auto-moderation or human review.

Deciding Enforcement Options

After reviewing the text, the platform moderators can take any of the following actions:

• Remove or edit violating content.

• Retain and publish appropriate content.

• Warn or ban the user.  

• Shadow ban the user or add them to a blocklist.

Record Keeping and Auditing

After a specific action has been taken, it's a good idea to maintain detailed logs of the moderation actions, like:

• What was removed/edited/reported?

• What guidelines were violated?

• Any user communication.

These logs can help in case of user appeals and future infractions.

Text Moderation Use Cases 

Text moderation plays a role across nearly every type of digital platform. Here are some use cases:

• Social media platforms: Moderation covers hashtags, bios, comments, and posts to prevent hate speech, harassment, or misinformation.

• Online communities and forums: Topic threads, replies, and even user signatures require some oversight to keep discussions respectful.

• Gaming environments: Usernames, in-app chat, and clan/group chats are monitored to reduce abuse and cheating.

• Dating apps: Bios and profile text must be checked to prevent harassment, scams, or offensive content.

• Customer support channels: Support tickets, live chat transcripts, and feedback forms are screened to protect agents from abusive messages.

• E-commerce platforms: Reviews, product Q&As, and seller feedback require moderation to block review bombing, spam, or misleading claims.

• Livestream chats: Real-time conversations during streams are fast and unfiltered, making them prone to offensive language, spam, or harmful comments that can disrupt entire events.

• Healthcare: Patient forums, support groups, and telehealth chat systems require strong safeguards so that misinformation, offensive remarks, or unauthorized sharing of protected health information (PHI) don't harm patients or violate compliance standards.

• Education: Online classrooms, student forums, and collaborative learning spaces need to be moderated to prevent bullying or cheating tips.

Best Practices for Text Moderation

Use Moderation APIs

Instead of building everything in-house, APIs give you ready-to-use moderation capabilities that are flexible and easy to scale. These come with the following features: 

• Custom blocklists for unique terms

• Semantic filters for disguised harm

• Harm categories mapped by severity

• Flexible actions to flag, block, and shadow ban

• Adjustable sensitivity for fine control

• Scalable automation with less overhead

Design for Localization and Multilingual Contexts

Moderation rules rarely translate one-to-one across languages.

Build systems that handle slang, regional terms, and cultural nuance to avoid blind spots.

Close the Feedback Loop

A static moderation system loses accuracy over time.

Feed user reports, appeals, and moderator decisions back into your models to keep improving detection.

Plan for Adversarial Behavior

Bad actors constantly look for workarounds, like misspellings, coded language, or memes.

To stay ahead, update detection rules and filters regularly.

Frequently Asked Questions