Blocklist

Modern platforms balance growth with safety, filtering countless interactions across chat, email, and network layers with blocklists. They can reduce abuse and noise while preserving legitimate traffic, consistently, transparently, and at machine speed.

What Is a Blocklist?

A blocklist is a predefined list of items that a system actively disallows or filters out. Its main purpose is to protect your users, platform, and brand.

Below are some of the most common types:

• Chat and other text-based blocklists review all incoming messages or posts, flagging or removing those containing restricted terms automatically.

• Email blocklists are DNS-based lists of sender IPs that mail servers use to reject or route spam, often supplemented by domain/URL lists such as URIBL or SURBL.

• IP/network blocklists are databases of IP addresses or networks considered malicious, untrustworthy, or outside of a restricted service area.

• URL/domain filters filter disallowed website domains, denying access to known malware, phishing sites, or unwanted advertising.

How Does a Blocklist Work?

Blocklists compare incoming user-generated content (UGC) or traffic against a set of forbidden items, then block the match or flag it for review. Although the implementation varies by context, here is the general workflow:

1. List setup: Developers or moderators compile a list of forbidden terms or addresses. This might be:

• Abusive words or patterns in chat or forum posts

• IPs or domains associated with scams for emails

• IP ranges or suspicious sources in networking

3. Scanning/matching requests: Incoming requests or messages are automatically checked against the list. For text-based UGC, many systems use pattern-matching algorithms. In email or network filtering, the sender's IP or domain is looked up in DNS-based databases.

4. Action: If a match is found, the system either blocks the item or flags it for review. Blocking means the request is denied, while flagging sends the message to a queue for human review.

5. Fine-tuning: Effective lists are updated regularly to ensure effectiveness. Email admins monitor spam complaints and may add new spammy IPs to their lists, while chat teams may update the regex patterns if users develop workarounds to bypass existing lists.

How Chat/Text Blocklists Work

Chat and other text-based lists consist of words, phrases, or patterns that go against your rules or community standards. What's blocked will vary depending on the platform, but it commonly covers language that is discriminatory, threatening, sexually explicit, or endorsing illegal acts. It can also cover spam.

For example, a gaming app might block phrases related to cheating or scamming, like "hack download", "win cash", and "bitcoin giveaway," while a dating site will put more emphasis on filtering out sexual or racial harassment terms.

You can hardcode the list during development, but creating a moderation dashboard where non-developers can manage it is better.

After setting up the words, the system uses regex to scan incoming messages against the forbidden text. Regex is a type of dynamic filtering that catches variations of blocked words that would bypass a traditional filter, like misspellings, extra or missing spaces, and character substitutions.

Common regex patterns include:

• h[a@]ck
  Catches variations with "a" or "@" — hack, h@ck

• h[a@4]ck
  Catches variations with "a," "@," or "4" — hack, h@ck, h4ck

• h\s*ack
  Catches any number of spaces between "h" and "ack" — hack, h ack, h ack

• h[a@]*ck
  Catches repeated "a" or "@" — hack, haack, h@ck, haaaaack

• h[a@]{2,}ck
  Catches if "a" or "@" appears two or more times — haack, h@@ck

• h\s*a\s*c\s*k
  Catches extra spaces between every letter — h a c k, h a c k

Based on the developer's settings, the system can either block matching messages outright or flag them for human moderators to review.

How Email/Domain Blocklists Work

When a mail server receives an email, it queries DNS-based blocklists (DNSBLs) for the sender's IP. If listed, the server rejects the message.

There are two main types of email lists:

• IP-based lists: Also known as real-time blacklists, these block the IP addresses from which emails originate. They target specific IP addresses that send junk or harmful mail, adding them to their lists to prevent further abuse. They can also block a defined range of IP addresses if multiple spam emails are received.

• Domain-based lists: These focus on the specific domains, regardless of the IP addresses. Domains are usually blocked if they're reported for large volumes of spam emails, such as if example.com was known for persistently sending unsolicited product advertisements.

Developers can create custom blocklist databases or use those provided by services like Spamhaus, Barracuda, Cloudmark, or SpamCop.

Internet browsers also often have built-in domain blocklists to protect users from visiting sites known to be unsafe. Chrome, Firefox, and some others use Google's Safe Browsing service. 

How IP/Network Blocklists Work

At this level, these lists deny network access to specific IPs or ranges. These are usually addresses that pose security threats.

When a user makes a request, firewalls and routers check the incoming connection's source against the list. If it's on there, the firewall will drop any data packets from it.

Some websites and apps may block IPs based on geographical location, such as an on-demand video service in one country that prevents outside access for licensing purposes.

Strict vs Flagging Blocklists

Depending on the type and your platform's content moderation policy, you may choose to make your list strict, flagging, or both.

Strict Lists

These automatically remove messages with blocked terms, acting swiftly against problematic content. They're ideal for real-time chat and livestreams, where the live nature of the platform can make it harder for mods to monitor every incoming message.

Flagging Lists

These lists flag items for review, rather than removing them outright. Platforms with active moderation, like marketplaces, primarily rely on this reactive approach, factoring in intent and context before removing a post or message.

Some companies may employ a mix of the two, especially for moderating text; they may use a strict list for clear-cut violations, like slurs, and a flagging one for borderline cases or temporarily forbidden topics, like terms that may be offensive in certain contexts.

Most lists will be strict outside of moderating UGC. However, some browsers may outright block some sites and only warn against others.

User-Defined Blocklists 

Social, gaming, and chat apps and email services often allow users to create their own lists. This puts some of the moderating effort into the hands of individuals, giving them the ability to prevent messages and emails or hide their posts from accounts with which they don't want to interact.

This could look like a player blocking another one in-game due to unpleasant interactions that didn't technically violate any rules, reducing conflict and making mods' jobs easier.

Benefits of Implementing a Blocklist

The benefits of this moderation tool include:

Efficiency

They provide a proactive approach, helping ward off threats before they cause harm, with minimal human intervention. Moderators would struggle to review every message in a social app with thousands or millions of users, but a blocklist can automatically detect violations as they occur.

User Experience

These lists enhance the quality of experience (QoE) by protecting users from harmful or unwanted content, creating a safer community, maintaining trust, and encouraging participation. For example, email lists clean up inboxes by keeping out spam, which directly improves user satisfaction and deliverability.

Consistency

Unlike human mods, predefined lists are free from unconscious bias and can enforce rules consistently. This creates a more transparent and predictable environment since all users are held to the same standard.

Security

They defend against spam, malware, and cyber attacks. Blocking known domains or IPs saves time and resources that would have been spent dealing with phishing or other attacks.

Best Practices 

These best practices will help you balance user safety and usability.

Review and Update Regularly Based on Data

Analyze metrics such as false positives, most blocked terms, blocked IP addresses, blocked domains, and the total number of flagged terms to make further refinements.

Going through this data helps you identify features to implement, such as semantic filters and new terms to add to the list. You can also set up automated alerts or periodic audits to catch any blind spots, such as increased reports from a user who found a workaround.

You must also revisit your lists often for accuracy and relevance. For chat, gaming, or social apps, carefully consider how blocking or allowing more words could impact the user experience. A phrase like "I hate losing" shouldn't be blocked just for including the word "hate."

Communicate Transparently

Transparency builds user trust. Displaying a clear message — like "your message likely contains spam" or "your message was blocked due to a community guideline violation" — helps users understand the rules and reduces frustration. 

In email or network contexts, proper bounce messages can help senders understand why you rejected them.

Cater to Your Audience

Tailor your text-based lists to your demographic. Factor in their ages, interests, backgrounds, and more as you decide what to block. An educational livestreaming platform will need stricter limits to protect kids compared to a niche hobby forum targeting adults. Similarly, if your users are multilingual, you must account for that in your regex.

If you have a wide mix, you can implement channel-specific rules. Your all-ages or kids' channels can have tighter restrictions, while your adult-only channels are more lax.

Leverage Multiple Layers

Combine them with other moderation tools. For example, use both keyword filters and AI-based detection to catch hate speech. For broader coverage, cross-reference multiple blocklists (such as word lists alongside a semantic engine). In email, combine IP lists with SPF/DKIM checks to authenticate senders.

Frequently Asked Questions