Wiktionary as a Crowdsourced Lexicon for English Dialects

Sidney Wong

cs.CLAug 16, 2026

Wiktionary as a Crowdsourced Lexicon for English Dialects

Authors: Sidney Wong

Organizations: Centre for Sustainability Research, University of Otago Te P¯unaha Matatini Centre of Research Excellence for Complex Systems, New Zealand

Abstract

This paper evaluates Wiktionary as an ethically crowdsourced lexicon for English dialects. We took a two-phase approach, providing an in-depth descriptive analysis of the crowdsourced lexicon for 12 national varieties of English before applying the lexicon to geo-referenced, country-level social media language data to examine the real-world performance of this crowdsourced dialect lexicon. We demonstrate that Wiktionary matches or exceeds the coverage of traditional dictionaries, such as the Oxford English Dictionary (OED), for regional and Outer-Circle varieties. Our dialect-specific case study on New Zealand English found high alignment between Wiktionary and the OED based on word-formation patterns (R = 0.883). Similarly, we observed high alignment between the dialect lexicon and geo-referenced social media language. While this paper found that Wiktionary has broad coverage of lexical properties, it also highlighted some of the macro-challenges involved in evaluating dialect-responsive language resources and tools, such as the role of language contact in dialects and register effects in web-based corpora.

Figures & tables

Category	$n$	Example
African	13	Botswanan, Cameroonian
Antarctic	-	-
Asian	3	East Asian, South Asian
Caribbean	8	Bahaman, Barbadian
Commonwealth	16	Australian, Bangaldeshi
European	6	British, Channel Islands

Table 1: Summary table of the Wiktionary categories of regional varieties including the number of subcategories (

n

) and examples.

Grouping	Country	Subreddit	Members	Submissions		Comments
Grouping	Country	Subreddit		$n$	$\bar{x}$	$n$	$\bar{x}$
Outer-Circle	India	r/india	3.2M	481K	64.5	12.5M	202.8
Outer-Circle	Pakistan	r/pakistan	575K	87K	56.9	2.4M	212.0
Outer-Circle	Malaysia	r/malaysia	1.3M	73K	55.3	3.5M	179.0
Outer-Circle	Philippines	r/Philippines	3.3M	331K	52.0	13.6M	138.7
Outer-Circle	Kenya	r/Kenya	219K	24K	42.3	475K	150.6

Table 2: Summary table of Reddit communities including the associated national variety of English (Country), the name of the community (Community), the number of subscribers as of June 2025 (Members), and the total number of words (

n

) and mean word length (

\bar{x}

) for Submission Posts and Comments.

Dialect	OED	Wiki	$\%\Delta$
Malaysian	19	450	2,268.4
Filipino	93	1,493	1,505.4
Canadian	550	2,834	415.3
Indian	721	1,816	151.9
Irish	1,145	2,105	83.8
Australian	2,435	3,381	38.9

Table 3: Summary table comparing the total number of entries from the OED and Wiktionary (Wiki) by national variety (Dialect).

Figure 1: Euler diagram illustrating the degree of overlap in Wiktionary entries for the six Inner-Circle varieties of English. Similar to Venn diagrams, a Euler diagram visualises sets and their relationships particularly those with complex hierarchies.

Figure 2: Euler diagram illustrating the degree of overlap in Wiktionary entries filtered by NZE and the six Inner-Circle varieties of English.

Word Formation Process	OED	Wiki	$\%\Delta$
Initialism	3	14	366.7
Blend	3	9	200.0
Backformation	3	6	100.0
Unknown	21	38	81.0
Proper name	37	66	78.4
Borrowing	287	399	39.0

Table 4: Summary table comparing the number of entries classified by word formation processes between the OED and Wiktionary (Wiki). The categories are ordered by percentage difference (

\%\Delta

) between OED and Wiktionary.

Dialect	Submission Posts			Comments
Dialect	Community	$\mu_{i}$	Alignment	Community	$\mu_{i}$	Alignment
British	r/unitedkingdom	5,475.2	Yes	r/malaysia	3,973.3	No
Irish	r/ireland	889.1	Yes	r/ireland	771.1	Yes
Australian	r/australia	743.4	Yes	r/australia	333.4	Yes
New Zealand	r/newzealand	829.5	Yes	r/Philippines	498.8	No
American	r/usa	5,538.7	Yes	r/canada	3,582.3	No

Table 5: Summary table presenting the Subreddit communities (Community) with the greatest mean relative frequency (

\mu_{i}

) and alignment by Inner-Circle variety of English (Dialect) grouped by text-type: Submission Posts or Comments.

Entry	Submissions	Comments
box of birds	-	11
box of fluffies	-	14
in the dogbox	-	7
jack a dandy	-	-
pack a sad	-	39
suck the kumara	1	5

Table 6: Table listing the total number of multi-word expressions (excluding borrowings from te reo Māori) in r/newzealand from Submission Posts and Comments.

Entry	r/newzealand		r/Philippines
Entry	$n$	$\mu_{i}$	$n$	$\mu_{i}$
mo	1,902	0.6	1,319,868	403.9
pa	3,338	1.1	1,174,769	359.5
para	372	0.1	499,040	152.7
eh	17,914	5.9	435,229	133.2
po	857	0.3	309,069	94.6

Table 7: Summary table including the token frequency (

n

) and mean relative frequency (

\mu_{i}

) for the top five NZE Wiktionary entries grouped by Subreddit communities.

Entry	Submissions	Comments
biddi biddi	-	-
hei tiki	-	42
ka pai	2	619
kapa haka	8	415
kia ora	121	2,645
kohanga reo	-	90

Table 8: Total number of multi-word expressions (borrowings from te reo Māori) in r/newzealand from Submission Posts and Comments.

Figure 3: Relative frequencies of lexical dialect features associated with the six Inner-Circle varieties of English across twelve Reddit communities in Submission Posts.

Figure 4: Relative frequencies of lexical dialect features associated with the six Inner-Circle varieties of English across twelve Reddit communities in Comments.

Language, Place, and Social Media: Geographic Dialect Alignment in New Zealand
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Language, Place, and Social Media: Geographic Dialect Alignment in New Zealand

Sidney Wong

University of Canterbury

Open paper AskPDF arXiv

This thesis investigates geographic dialect alignment in place-informed social media communities, focussing on New Zealand-related Reddit communities. By integrating qualitative analyses of user perceptions with computational methods, the study examines how language use reflects place identity and patterns of language variation and change based on user-informed lexical, morphosyntactic, and semantic variables. The findings show that users generally associate language with place, and place-related communities form a contiguous speech community, though alignment between geographic dialect communities and place-related communities remains complex. Advanced language modelling, including static and diachronic Word2Vec language embeddings, revealed semantic variation across place-based communities and meaningful semantic shifts within New Zealand English. The research involved the creation of a corpus containing 4.26 billion unprocessed words, which offers a valuable resource for future study. Overall, the results highlight the potential of social media as a natural laboratory for sociolinguistic inquiry.

Abstract

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