How to create a popular word game that players trust

A game can have the world’s most engaging interface, but the data powering it determines whether players return or get frustrated and delete the app.

Popular word games do well because users trust that each word is real, findable, and pitched at the right level of difficulty. But that trust is fragile. Players notice mistakes, or unfairly obscure words, and won’t hesitate to call the game out online.

In this article, we explore the five essential steps for building a word game and how to get them right. This is grounded in Oxford Languages’ 150+ years of expertise in providing authoritative language data used by word games such as UniWords and The New York Times Games.

How do word games work?

Word games are built on a simple loop: player inputs a word, the system validates it, and the game responds. Behind that loop is a stack of language data: a validated word list that confirms whether the submission is a real word, a difficulty layer that determines which words appear in which rounds, and sometimes a definition layer that reveals meaning after each turn. Every player interaction depends on these data systems working accurately and invisibly.

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What powers a word game technically

Before any player makes a move, three interconnected data systems need to be in place.

Layer	What it does	What breaks without it
Word validation	Confirms submitted words are real and acceptable	Players dispute the results; the game loses authority
Definitions	Powers hint systems, reveal screens, and learning modes	Educational value disappears; context is lost
Difficulty grading	Calibrates puzzle progression and word selection	Players quit, either bored or overwhelmed

These layers are not independent. A word that passes validation but has no associated definition cannot support a hint system. A word graded as ‘easy’ based on frequency data but missing from the validation list cannot be played. The three systems need to be built together from shared source data.

How to create word games: a step-by-step guide

Step 1: Build your word list on validated data

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The first mistake many developers make is assuming that any large word list will suffice. A raw dictionary export, a web scrape, or a public corpus may contain hundreds of thousands of entries but also include archaic terms, proper nouns, offensive words, and regional variations that cause disputes the moment players encounter them.

Validation datasets serve a different purpose. They are specifically curated to answer one key question: “Is this a word that should be accepted in the game?”

Oxford Languages’ validation datasets for gamified learning are comprehensive and customizable, created by expert language technologists rather than assembled from web data that can be unreliable.

The practical requirements for a validation dataset vary by audience. A children’s educational game requires sensitivity labels and age-appropriate vocabulary. A competitive adult game must handle regional spelling variants, ensuring that ‘color’ and ‘colour’ produce consistent results across locales. A multilingual game needs validated lists in each target language, not machine-translated approximations of English word lists.

What to demand from a validation dataset:

• Comprehensive lists with root words and inflected forms
• Sensitivity and offensive word labels that can be toggled by the audience
• Regional variant coverage, with locale classification
• Human-curated provenance, not assembled from unverified web text
• Customizable scope, so you include only what your game design requires

The Scrabble word list debate is a good example. When Hasbro updated the Official Scrabble Players Dictionary to remove certain offensive words, competitive players voiced strong opinions, with some even threatening to leave the association.

Word list decisions are never neutral; they reflect editorial choices that impact players. Starting with a curated, sensitivity-labelled dataset provides a solid foundation, not a potential minefield.

Step 2: Use definitions to add meaning, not just answers

A word list tells a game what to accept; definitions tell players why they should care. These two elements serve entirely different purposes, and confusing them is a common mistake in early-stage game development.

In a basic validation context, definitions may be optional. However, in any game featuring a hint system, post-round reveals, learning modes, or educational objectives, they become crucial.

A player who submits “quaff” and receives the definition “to drink heartily” leaves the game having learnt something new. A player who receives no definition, however, only knows that their word was accepted.

The Word Games Developer Toolkit from Oxford Languages brings together high-quality, human-curated word lists and flexible integration tools built specifically for word game development. It offers multilingual coverage across seven languages, content sensitivity controls, and straightforward implementation, all backed by Oxford’s lexicographic expertise.

Definitions are organized by current frequency of use, meaning the primary sense shown reflects the most common usage today, rather than an archaic or historical sense.

This ordering is vital in a game context. A hint system that presents the most archaic sense first will confuse players. In contrast, one that highlights the most contemporary sense gives players the best chance of recognizing the word they’ve played.

For educational word games, definitions unlock a distinct product category. UniWords, for instance, builds Oxford Languages’ definitions directly into gameplay, giving players the meaning behind each word they submit rather than a simple accept or reject.

This definition layer transforms a word game from a simple scoring mechanism into a learning tool, opening doors to different markets and age groups.

Step 3: Grade difficulty using frequency data

Difficulty grading is often the most misunderstood aspect of word game design, but it is crucial for retention. If a game is too easy, players lose interest; if it’s too hard, they abandon it. The balance between these outcomes relies largely on how difficulty is measured.

Frequency, how often a word appears in real written and spoken language, is the most reliable predictor of perceived difficulty. Words that players encounter regularly feel accessible, while those they’ve never seen in print seem unfair, regardless of their technical validity.

Research published in Knowledge and Information Systems (Shi et al., 2024) analyzed Wordle difficulty using a model based on attributes like frequency, letter repetition, and part of speech.

The model achieved 95% accuracy in predicting difficulty tiers, with frequency being the dominant variable. This aligns with what most game designers intuitively know: familiarity drives fairness.

Wordle was created by a software engineer, Josh Wardle, for his partner, who sorted through 12,000 words based on whether or not she knew them. Today, The New York Times still applies this familiarity principle to Wordle’s hand-curated list of common, high-frequency words. Low-frequency words are allowed as guesses but not included in the plausible solutions list of 3,200 everyday words. The Wordle Bot determines whether a word is likely to be an answer based on its frequency in New York Times archives dating back to the year 2000. This ensures the game stays engaging and enjoyable for players.

Tip: Oxford Languages’ frequency data, available via the Word Games Developer Toolkit, offers per-million frequency figures drawn from a contemporary English language corpus consisting of over a billion words. This provides developers with precise, corpus-backed frequency data, allowing for accurate difficulty tiers rather than approximations.

Step 4: Handle edge cases before players find them

Players will always discover the edge cases that a game’s data doesn’t cover. The most common sources of dispute are predictable, and each has a data-level solution.

• Proper nouns and abbreviations: A validation dataset should clearly specify whether proper nouns are allowed. Consistency is key as players notice when rules are applied inconsistently.
• Inflections and plural forms: If “cat” is in the word list, is “cats” valid? If “run” is listed, is “running” acceptable? Oxford Languages’ Word Games Developer Toolkit combines lexical and morphological information to provide all the inflected forms of each lemma, with explicit labels for “lemma” and “inflection” so developers can easily ingest the list without needing further manual annotation.
• Regional spelling variants: “Colour” and “color” are the same word, as are “organise” and “organize”. An inclusive and family-friendly game that accepts one but not the other, based on an inconsistent dataset, will frustrate players in the variant’s home region. Oxford Languages’ dialect datasets include lexical locale classification for British, American, Australian, Indian, and World English variants, so developers can configure acceptance rules by region instead of guessing.
• Offensive and sensitive words: This is the non-negotiable edge case. A game that surfaces an offensive term as an answer will quickly draw negative attention. Sensitivity labels applied at the dataset level, rather than filtered ad-hoc, offer a clean, maintainable solution for developers.

Step 5: Keep your word list current

Language is constantly evolving. Terms like “rizz”, “situationship”, and “generative AI” have entered common usage in just the past few years. A word game based on a static dataset will reject contemporary words that players know are real, leading to disputes that erode trust in the same way that accepting obscure, archaic words does.

This isn’t just a minor maintenance issue; it’s a structural product risk. Words of Wonders by Fugo Games achieved 60 million installs in 18 months through aggressive localization for emerging markets. However, localization only succeeds when the underlying word data reflects current usage in each locale, not a snapshot from years ago.

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Oxford Languages’ datasets are continuously updated through the same editorial process that adds new words to the Oxford Dictionary of English.

Expert lexicographers regularly incorporate new entries, updated frequency rankings, and revised sensitivity labels into the Word Games Developer Toolkit. This means developers can choose to annually refresh their word data without rebuilding their validation, definition, and difficulty systems from scratch.

When it comes to product roadmaps for word games, word data should be treated as a live dependency, not a one-time procurement. It’s far more cost-effective, and a better user experience, to build that into the product early than to fix it later, once players have already spotted outdated words.

How UniWords built a word game with Oxford Languages

UniWords, a classic word game for iOS and Android, licensed Oxford Languages’ UK English word game dataset.

UniWords knew they needed to work with a provider who could offer trusted, verifiable data so that players could have confidence in the fairness and transparency of the game. As UniWords CEO Alexander Stobbe put it:

“We could have found a word list in the public domain. However, such a word list would not inspire the same confidence as data provided by Oxford Languages.”

Using part-of-speech data, UniWords can tell players exactly why a word was rejected, flagging proper nouns and other invalid categories. The same dataset powers word frequency indicators, so players can see how rare or common a valid word is, and definitions are built directly into gameplay.

Integration was fast. As UniWords software engineer Sebastian Brandt described it:

“The data arrived in a clean, well-structured format that we could implement directly into our word game without any pre-processing or transformation.”

How NYT Games uses Oxford Languages data

The New York Times’ games team, which builds titles including Crossplay, relies on Oxford Languages for the dictionary layer behind gameplay. As Zoe Bell, Executive Producer of Games, put it:

“Having an excellent dictionary is key to any word game. It’s not just about definitions, but also about using other available data fields like offensiveness and pronunciation to make your game feel more thoughtful. We use Oxford in games like Crossplay to achieve those goals.”

This reflects a pattern across the industry: teams that get word game data right treat it as a system spanning definitions, sensitivity, and pronunciation, not a single word list. This also helps them avoid uncouth or sensitive words. You can read all about how the New York Times selects vocabulary for word games in their recent article “Why you sometimes see naughty words in NYT games”.

Oxford Languages products for word game developers

Oxford Languages’ Word Games Developer Toolkit provides the full data stack word games depend on, with flexible delivery to match different technical requirements.

Feature	What it provides	Best suited for
Word lists	Human-curated, de-duplicated word lists across seven languages	Word acceptance layer
Sensitivity controls	Sensitivity markers and filtering options to flag or exclude offensive terms	Brand-safe, family-friendly gameplay
Difficulty range	Vocabulary spanning familiar to challenging, supporting varied player profiles	Difficulty grading and puzzle curation
Customization	Configurable lists aligned to specific themes, mechanics, or design goals	Tailored word challenges and themed modes

Build on data that players will trust with Oxford Languages

Word games rely on players’ confidence that the game understands the full range and intricacies of the language and will never present a result that feels arbitrary or incorrect.

This trust is built on data that’s crafted with the same rigour players expect from the game itself. Oxford Languages has provided the language infrastructure behind Words with Friends and other leading game products. The same data stack is now available to development teams creating the next generation of word games.

Explore the Word Games Developer Toolkit or speak to the team to discuss the right configuration for your product.