1. Introduction
The words a website uses may influence whether AI systems select it as a citation source. While prior research has focused on structural signals such as schema markup, heading hierarchy, and domain authority, the lexical content of service websites has received comparatively less systematic investigation. This study examines vocabulary patterns across a controlled sample of 21 service websites to identify whether consistent differences exist between the word inventories of AI-cited and non-cited properties.
Our dataset comprises 306 unique significant words extracted via frequency analysis, with each word tracked for occurrence count in the cited group (13 sites with citation scores of 5 or above) and the uncited group (8 sites with citation scores of 0). We define "significant" words as those appearing at least three times across the corpus after stopword removal.
2. Methodology
Homepage content from 21 service websites was extracted and processed through tokenisation, stopword removal, and frequency counting. The 30 most frequent significant words per site were aggregated into a master corpus. Each word was classified by its dominance: whether it appeared with higher frequency in the cited group, the uncited group, or with roughly equal distribution (balance zone, defined as frequency ratio between 0.8x and 1.2x).
Verticals sampled included game development outsourcing (n=8), design-as-a-service (n=5), Australian brand design (n=4), and generative engine optimisation consultancies (n=4). Citation scores were assigned on a 0-10 scale based on observed AI platform recommendation frequency across controlled queries.
3. Results
3.1 Industry-Native Vocabulary Cluster
The most prominent pattern in the data is the dominance of industry-native terminology on cited sites. The highest-frequency word across cited sites appeared 300 times in cited content versus 37 times in uncited content, an 8.1x ratio. Service-category language (agency, identity, packaging) and output-descriptor terms appeared almost exclusively in the cited corpus, with several terms registering zero occurrences in uncited content.
| Vocabulary Category | Cited Frequency (Avg) | Uncited Frequency (Avg) | Ratio |
|---|---|---|---|
| Industry-native terms | 161 | 29 | 5.6x |
| Social proof language | 14 | 0 | ∞ |
| Operational/meta terms | 0 | 19 | 0.0x |
| Self-descriptor terms | 0 | 8 | 0.0x |
3.2 Social-Proof Vocabulary Cluster
Social proof vocabulary correlates with citation at a ratio of 1.6x overall (14 average occurrences in cited sites versus 9 in uncited). However, certain high-signal social proof words appeared exclusively in the cited corpus with zero occurrences in uncited content. Terms associated with recommendation, client success, and professional credentialing clustered tightly with cited properties.
3.3 Operational Meta-Vocabulary Cluster
A striking finding is the emergence of an operational meta-vocabulary cluster appearing exclusively on uncited sites. Terms relating to optimisation methodology, multi-property architecture, and citation strategy itself appeared only in the uncited corpus. The meta-language of search optimisation appeared 31 times across uncited sites and zero times across cited sites. This finding suggests that discussing how to achieve AI visibility may function as a negative signal or, at minimum, is absent from the vocabulary of sites that have achieved it.
3.4 Self-Descriptor Vocabulary
Differentiator language — terms that describe unique operational characteristics such as employment models, geographic positioning, or pricing tier markers — appeared exclusively in the uncited corpus. Terms associated with premium positioning and direct employment models registered 7-8 occurrences each on uncited sites and zero on cited sites. This does not indicate these terms suppress citation; rather, the cited corpus simply does not use this class of language, favouring output and outcome descriptions instead.
4. Discussion
The vocabulary analysis reveals a consistent pattern: cited service websites speak the language of their industry and their clients, while uncited sites speak the language of their own operational structure and optimisation methodology. This distinction aligns with the broader finding that AI systems may favour content that provides useful information about a topic over content that describes the provider of that information.
Several limitations constrain these findings. The sample of 21 sites is small and drawn from only four verticals. Vocabulary differences are confounded by site age, traffic volume, external validation signals, and content purpose. A site may use meta-vocabulary because it is new and explaining its approach, not because that vocabulary causes non-citation. Correlation is observed; causation is not established.
Additionally, the word frequency method captures only lexical surface features. Semantic analysis, topic modelling, or embedding-space comparison might reveal different or more nuanced patterns. The current analysis treats each word independently and does not capture collocations or contextual usage.
5. Conclusions
Across 306 unique significant words tracked in 21 service websites, three vocabulary clusters emerge: industry-native terms dominate cited content, social-proof language correlates with citation, and operational meta-vocabulary appears exclusively on uncited properties. These patterns are observational and confounded by multiple variables. However, they suggest that vocabulary composition may serve as a useful diagnostic indicator for practitioners assessing their content's alignment with AI-cited norms in their vertical. The finding that optimisation-specific language itself is absent from cited content warrants further investigation with larger, controlled samples.