Building A Slur Database Is A Lot Harder Than Dumping Words Into A CSV
I spent about eighteen months working on a project that involved maintaining a database of racially charged terms across multiple dialects and regional variants. It was supposed to be straightforward. It wasn’t. The first mistake most people make is treating this like a vocabulary exercise. It’s not. It’s an exercise in understanding how language changes when people are trying to disguise speech, how regional spelling shifts matter, and how context can turn a neutral word into something completely different depending on who says it and where. The core challenge isn’t collecting the words. Anyone can scrape a forum or copy a list from an activist site. The challenge is deciding what goes in, what stays out, how you tag severity, and what you do with the data once it’s there. A slur database isn’t a reference book. It’s a tool that will be used for filtering, research, moderation policy, and sometimes legal proceedings. The accuracy of your decisions depends entirely on how carefully you built it. My first version had roughly fourteen thousand entries. I felt proud of the scale. Then I ran it against actual community moderation logs and found that about sixty percent of the false positives came from words that weren’t slurs at all but happened to share characters with entries in my database. "Natalie" triggered on "nazi" variants. "Cracker" flagged a bakery blog. "Hook" hit fishing forums because of phonetic similarity to a different slur in a dialect I hadn’t accounted for. I had to cut the database down to about nine thousand entries and rebuild the matching logic from scratch.
Here’s what I learned about the curation process itself.
Source Collection
You need multiple source types. No single source gives you complete coverage. The ones that matter most are: When I built my first dataset, I relied too heavily on activist GitHub repos. They were fast to update but inconsistent in quality. Some entries had detailed etymologies and regional tags. Others were just a word with a severity score of 7 out of 10. I ended up cross-referencing everything against academic sources before trusting any entry. It added about three weeks to the initial build but saved me months of cleanup later. Each entry needs more than a word. A basic flat file won’t hold up in practice. Here’s the schema I ended up using after the redesign:
Get the Full Details
Term: the base spelling
Variants: alternate spellings, leetspeak forms, phonetic approximations
Region: where the term is primarily used (US South, UK, Caribbean, etc.)
Target: who the slur is directed at (race, ethnicity, religion, nationality, etc.)
Severity: a scale from 1 to 10 based on historical harm and current intensity
Context tags: formal vs informal, in-group reclamation status, generational use patterns
Source: where the entry came from
Date added: when the entry was first included
Last verified: date of last source check The in-group reclamation tag is the one most people skip and then regret. A term that one group uses as a slur against another group may be completely normalized within that group. The word "cracker" is a good example in certain Southern US contexts. It’s used differently depending on who’s saying it and to whom. If your database flags it uniformly, your filter will break in places where the term is actually benign. I learned this the hard way when a school district asked me to help configure content filters for their student network. The initial model blocked a student's project about Appalachian folk music because one of the tracks contained a regional term that appeared in my database without the in-group reclamation tag. The fix took two days of manual review and retagging. I added that field to every entry after that.
Matching Logic
Exact string matching is insufficient. You need fuzzy matching with controlled parameters. The approach that worked for me used several layers: First layer is exact match against the base term and all recorded variants. This catches standard usage. Second layer is phonetic matching using Soundex and Metaphone variants, but only within the same regional context. A phonetic hit between a term from the American South and one from the UK is less meaningful than a phonetic hit within the same region. Third layer is substring matching with a minimum character threshold. This catches cases where someone breaks up a slur with spaces or punctuation, which is common in moderated environments where users are actively evading filters. The tricky part is setting thresholds that don’t create cascading false positives. I spent about six weeks tuning the fuzzy match parameters. The solution wasn’t a single algorithm. It was a weighted scoring system where each match type contributed differently to a final confidence score. Only matches above a certain confidence threshold triggered action. Everything in between went to a review queue.
This cut my false positive rate from about twenty-two percent down to under four percent on the same test set. The review queue handled the edge cases that the algorithm couldn’t resolve with confidence.
Maintenance And Decay
Language changes faster than most people expect. A term that was highly charged in 2018 might have faded in certain communities by 2023, or it might have migrated to a new demographic. I stopped updating my database quarterly and started monthly after noticing that about fifteen percent of entries became stale within a year. Stale doesn’t mean wrong. It means the context around the word has shifted enough that the original severity rating or usage notes no longer apply. The workaround I settled on was a decay tracking system. Each entry gets a confidence score that starts at one hundred and drops slightly with each day it goes without a source verification. When it drops below sixty, the entry gets flagged for review. When it drops below forty, it’s quarantined until someone confirms the current state of the term. This prevented the database from drifting into obsolescence without requiring constant manual attention. I also discovered that some terms enter active circulation faster than academic sources can document them. The gap between a term appearing in a Discord server and showing up in a linguistics journal can be eighteen to twenty-four months. For a moderation tool, that gap is unacceptable. I added a rapid-deployment track for emerging terms that gets lower severity ratings initially and requires confirmation before the rating can increase. This is the opposite of most people’s instinct. They want to be conservative and slow. In this case, being too conservative means the database is irrelevant by the time it’s considered authoritative.
Data Storage And Access
I used PostgreSQL with a JSONB column for the variable fields like variants and context tags. Full-text search is built in and works well for this type of data. The database ended up at about two hundred megabytes with compressed text fields, which is manageable for most hosting providers. The real complexity wasn’t storage. It was query performance when the matching logic runs at scale. For a moderation pipeline processing thousands of messages per minute, I implemented a two-pass system. The first pass uses approximate matching on a lightweight cache to identify potential hits quickly. The second pass does the full rigorous matching only on candidates. This reduced average query time from about eighty milliseconds to under twelve milliseconds per message. Access controls matter more than people realize. This data can be weaponized. I made sure to include geographic restrictions in the API so that terms tagged as region-specific wouldn’t surface in unrelated contexts. A slur that’s primarily used in the US Southeast shouldn’t be auto-flagging content in Australian forums just because the base term exists in the database.
Common Failures
The biggest failure mode is treating this as a binary classification problem. Language isn’t binary. The same term can be harmless in one sentence and deeply offensive in another. My initial model scored every match as either blocked or allowed. That broke immediately in any real environment. I moved to a five-level output: block, quarantine, flag for review, allow with note, and allow. The middle three levels require human judgment and should never be automated away entirely. Another failure is over-indexing on one language or region. I saw a team in Europe build a database that was almost entirely American English slurs and then wonder why their moderation system failed completely on French and Arabic language content. If you’re building a database for a specific community, scope it to that community. Don’t pretend it’s universal. The third failure is ignoring the emotional labor involved in building this. People who compile these lists are often relaying trauma. They’re cataloging language that has been used to harm them or people they know. The database itself is neutral. The work of building it isn’t. I made sure anyone working on the curation team had access to support resources and rotation schedules that limited how many hours per week they spent on direct review of offensive content.
A Working Example
Here’s what a single entry looks like in the final schema after the redesign: Term: the base form
Variants: ["form1", "form2", "leet_speak_form"]
Region: ["US_Southeast", "US_National"]
Target: ["Black_American"]
Severity: 9
Context_tags: ["high_offense", "historical_weight", "not_reclaimed_in_target_group"]
Source: ["academic_paper_2019", "community_submission_2021"]
Date_added: "2021-03-15"
Last_verified: "2024-08-22"
Confidence_score: 87 The confidence score here reflects that the term has been verified twice in the last year and appears in both academic and community sources. An entry with a score of forty would have only one source and no recent verification. The system treats those entries differently in matching and severity calculations.
What I Wouldn’t Do Again
I wouldn’t start with scale. The first version’s fourteen thousand entries were a mistake. I’d start with about two thousand carefully curated entries across five regions and expand from there. Quality matters more than quantity in the early stages. A smaller database that works well is infinitely more useful than a large one that generates noise. I also wouldn’t skip the community review phase. Before deploying any version to production, I’d run the entries past people from the communities the terms target. They’ll catch nuance, reclamation status, and regional differences that external researchers will miss. I lost about three months because I didn’t do this on the first build. The revised database took six months total including community review, but it actually worked. The hardest part of this work isn’t the technical challenge. It’s deciding what level of completeness you’ll accept and sticking with it. A slur database is never finished. It’s always behind the language it’s trying to catalog. The goal isn’t perfection. It’s being accurate enough to be useful and honest about where the gaps are.
