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arxiv: 2604.15744 · v1 · submitted 2026-04-17 · 💻 cs.CL

Recognition: unknown

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

Sidney Wong
Authors on Pith no claims yet

Pith reviewed 2026-05-10 08:31 UTC · model grok-4.3

classification 💻 cs.CL
keywords communitieslanguagealignmentdialectgeographicmediaplacesemantic
0
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The pith

New Zealand Reddit users link language to place and form contiguous speech communities with complex geographic alignment; Word2Vec embeddings reveal semantic variations and shifts in NZ English on a 4.26 billion word corpus.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The work mixes qualitative checks on how people think language connects to location with computer models that turn words into numbers to track meaning. It looks at word choice, grammar patterns, and overall sense in online groups tied to specific New Zealand places. Static embeddings capture current differences between these groups while diachronic versions track how word meanings have shifted over time in New Zealand English. A very large set of raw text from these communities was assembled to support the analysis and future projects. The results indicate that place-based online groups behave like neighboring speech communities, yet the exact match to real geographic dialects is not straightforward. This approach treats social media as a living record of how language varies and evolves with identity and location.

Core claim

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.

Load-bearing premise

That Reddit communities tied to places accurately represent geographic dialect communities and that user perceptions of language-place links correspond to measurable patterns in actual language use.

Figures

Figures reproduced from arXiv: 2604.15744 by Sidney Wong.

Figure 2.1
Figure 2.1. Figure 2.1: Tripartite Model of Place Description: Tripartite model of place from Agnew (1987) as adapted from Reed (2020a). The three fundamental components are linked to setting, connection, and identity. Sense of Place Canadian geographer Edward Relph explained that place “has a range of subtleties and significances as great as the range of human experiences and intention” (Relph, 1976, p.26). This suggests there… view at source ↗
Figure 2.2
Figure 2.2. Figure 2.2: Sociotheoretical perspectives adapted from [PITH_FULL_IMAGE:figures/full_fig_p075_2_2.png] view at source ↗
Figure 4.1
Figure 4.1. Figure 4.1: Screenshot of Selfpost 155d1f3 Evaluation As a tool of inquiry, discourse analysis provides the data to support a given qualitative hypothesis (Gee, 2005). Based on SQ1, my aim is to determine whether users in place￾based communities associate language use with place identity. Firstly, I predict users do associate their place identity with their language use. I also predict users are intuitively attuned … view at source ↗
Figure 4.2
Figure 4.2. Figure 4.2: Screenshot of Selfpost 2 (1102c68) 81 [PITH_FULL_IMAGE:figures/full_fig_p113_4_2.png] view at source ↗
Figure 4.3
Figure 4.3. Figure 4.3: Selfpost 2 (1102c68): Cognitive I-Statements used the selfpost as a platform to initiate a discussion with the users of r/newzealand, as indicated by the Discussion post flair. Not only did the OP want the users to engage with the selfpost through upvoting, but the OP also encouraged comments through their open￾ended question in the coda of sub-story 1: What else have you seen US or otherwise?. By includ… view at source ↗
Figure 5.1
Figure 5.1. Figure 5.1: Clustering User-informed Variables across Country-level Communities [PITH_FULL_IMAGE:figures/full_fig_p159_5_1.png] view at source ↗
Figure 5.2
Figure 5.2. Figure 5.2: Clustering User-informed Variables for r/newzealand Description: This scatterplot visualises user-informed lexical variables for conservative variants within r/newzealand, utilizing k-means clustering (k = 2) and PCA for dimensionality reduction on token fre￾quency (n) and proportions. The analysis reveals two distinct groupings: Cluster 0 (blue), consisting of low token frequency and non-dominant conser… view at source ↗
Figure 5.3
Figure 5.3. Figure 5.3: Emergence and Growth of nek minnit Description: This lineplot visualises the emergence and growth of the lexical item nek minnit and its variants over time within r/newzealand. The feature first emerged in 2011, followed by a decline in usage in 2013, after which it has shown a consistent increase in frequency through to 2024; all data is sourced from Reddit/Pushshift (Baumgartner et al., 2020). 130 [PI… view at source ↗
Figure 5.4
Figure 5.4. Figure 5.4: User Engagement by Hour between Communities [PITH_FULL_IMAGE:figures/full_fig_p170_5_4.png] view at source ↗
Figure 5.5
Figure 5.5. Figure 5.5: Proportional Frequency by Hour (tramp and tramping) Description: This figure illustrates the proportion of user-informed variables tramp (a) and tramping (b) over a 24-hour period within r/newzealand comments (rcomm). Observations indicate a minor increase in the proportional frequency of the innovative variants hike and hiking between 12:00 and 18:00 UTC, a window corresponding approximately to midnight… view at source ↗
Figure 5.6
Figure 5.6. Figure 5.6: Proportional Frequency by Hour (Other Variables) [PITH_FULL_IMAGE:figures/full_fig_p172_5_6.png] view at source ↗
Figure 6.1
Figure 6.1. Figure 6.1: Lineplots of Classifier Performance by Sample Size [PITH_FULL_IMAGE:figures/full_fig_p186_6_1.png] view at source ↗
Figure 6.2
Figure 6.2. Figure 6.2: Stability of Dialect Classification Over Time [PITH_FULL_IMAGE:figures/full_fig_p187_6_2.png] view at source ↗
Figure 6.3
Figure 6.3. Figure 6.3: Most Similar to tramp in NewZealand Description: This visualisation presents the top 30 words most similar to tramp by cosine similarity within the Word2Vec embedding model trained on New Zealand city-level communities (NewZealand) using the skip-gram with negative sampling architecture. The terms most similar to tramp are hiking, kayaking, skiing, and surfing; all data is sourced from Reddit/Pushshift (… view at source ↗
Figure 6.4
Figure 6.4. Figure 6.4: Most Similar to tramp in Google300 Description: This visualisation presents the top 30 words most similar to tramp by cosine similar￾ity within the Word2Vec embedding model trained on the Google News dataset (Google300; Mikolov et al., 2013). The highest-ranking terms include dipsomaniac, boozehound, swineherd, swagman, and doss_house; data is sourced from the Google300 corpus (Mikolov et al., 2013). 174… view at source ↗
Figure 6.5
Figure 6.5. Figure 6.5: Clustering Analysis of tramp across Embedding Models Description: This figure displays subfigures comparing the NewZealand and Google300 models for the term tramp, utilising dimensionality reduction via PCA and k-means clustering of semantically similar keywords. The analysis includes a conservative variant (mountain, trek, hike, trail), an innovative variant (poverty, homeless, street), and neutral term… view at source ↗
Figure 7.1
Figure 7.1. Figure 7.1: Network Graph of Related Communities Description: This network graph visualises communities related to r/newzealand based on user engage￾ment, originally developed by u/avanka to measure Jaccard similarity among users posting across multiple communities between August and September 2018. The visualisation demonstrates that users active on r/newzealand also engage with other place-based and special intere… view at source ↗
Figure 7.2
Figure 7.2. Figure 7.2: User Engagement by Hour in New Zealand-related Communities [PITH_FULL_IMAGE:figures/full_fig_p219_7_2.png] view at source ↗
Figure 7.3
Figure 7.3. Figure 7.3: User Engagement in New Zealand-related Communities ( [PITH_FULL_IMAGE:figures/full_fig_p220_7_3.png] view at source ↗
Figure 7.4
Figure 7.4. Figure 7.4: User Engagement in New Zealand-related Communities ( [PITH_FULL_IMAGE:figures/full_fig_p221_7_4.png] view at source ↗
Figure 7.5
Figure 7.5. Figure 7.5: Example of a Syntactic Construction Description: This barplot (a) and lineplot (b) illustrate the frequency and temporal distribution of the syntactic construction syn:ADP – syn:the – syn:<200>, which captures New Zealand-related political discourse with a specific focus on Te Tiriti o Waitangi (the Treaty of Waitangi). Primarily sourced from r/newzealand and r/ConservativeKiwi, the construction exhibits… view at source ↗
Figure 7.6
Figure 7.6. Figure 7.6: Example of All (Combined) Construction Description: This barplot (a) and lineplot (b) illustrate the combined frequency and temporal trajectory of the construction sem:the > sem:<1089> related to the Covid-19 pandemic, where C2xG captures both syntactic and semantic constraints. Primarily sourced from r/newzealand and r/ConservativeKiwi, the construction emerged in 2019 and reached peak usage in 2022; th… view at source ↗
Figure 7.7
Figure 7.7. Figure 7.7: Scatterplot of Users Ranked by Score Description: This scatterplot displays the score and rank of users within city-level communities, sorted in descending order, revealing a noticeable Zipfian power law distribution among users with the highest scores; all data is sourced from Reddit/Pushshift (Baumgartner et al., 2020). a long right tail. Consistent with [PITH_FULL_IMAGE:figures/full_fig_p227_7_7.png] view at source ↗
Figure 7.8
Figure 7.8. Figure 7.8: Boxplot of Behavioural Measures Description: This boxplot visualises the distribution of users’ lifespan in years and engagement ratios by decile within city-level communities, illustrating that both behavioural measures follow a long right-tail distribution. The most long-lived users in the c1 decile exhibit a mean lifespan between four and six years, whereas younger cohorts from c6 onwards have a lifes… view at source ↗
Figure 7.9
Figure 7.9. Figure 7.9: Cosine Similarity of Lexical Features by Lifespan Cohort [PITH_FULL_IMAGE:figures/full_fig_p232_7_9.png] view at source ↗
Figure 7.10
Figure 7.10. Figure 7.10: Cosine Similarity of Syntactic Features by Lifespan Cohort [PITH_FULL_IMAGE:figures/full_fig_p233_7_10.png] view at source ↗
Figure 7.11
Figure 7.11. Figure 7.11: Cosine Similarity of Lexical Features by Engagement Ratio [PITH_FULL_IMAGE:figures/full_fig_p234_7_11.png] view at source ↗
Figure 7.12
Figure 7.12. Figure 7.12: Cosine Similarity of Syntactic Features by Engagement Ratio [PITH_FULL_IMAGE:figures/full_fig_p235_7_12.png] view at source ↗
Figure 7.13
Figure 7.13. Figure 7.13: Observations by Lifespan and Engagement Ratio Cohorts [PITH_FULL_IMAGE:figures/full_fig_p240_7_13.png] view at source ↗
Figure 7.14
Figure 7.14. Figure 7.14: Cosine Similarity of Lexical Features from [PITH_FULL_IMAGE:figures/full_fig_p242_7_14.png] view at source ↗
Figure 7.15
Figure 7.15. Figure 7.15: Cosine Similarity of Syntactic Features from [PITH_FULL_IMAGE:figures/full_fig_p243_7_15.png] view at source ↗
Figure 7.16
Figure 7.16. Figure 7.16: Network Graphs for rstext by Lexical Features Description: This figure displays subplots visualising network graphs for selfpost body texts (rstext) based on cosine similarity (a) and latent communities (b) using C2xG lexical features (lex), with cosine similarity below 50%. Nodes are colour-coded to represent r/newzealand in black, six city-level commu￾nities in blue, 14 peripheral communities in red, … view at source ↗
Figure 7.17
Figure 7.17. Figure 7.17: Network Graphs for rcomm by Lexical Features Description: This figure displays subplots visualising network graphs for comments (rcomm) based on cosine similarity (a) and latent communities (b) using C2xG lexical features (lex), with cosine similarity below 50%. Nodes are colour-coded to represent r/newzealand in black, six city-level communities in blue, 14 peripheral communities in red, and remaining … view at source ↗
Figure 7.18
Figure 7.18. Figure 7.18: Network Graphs for rcomm by Syntactic Features Description: This figure displays subplots visualising network graphs for comments (rcomm) based on cosine similarity (a) and latent communities (b) using C2xG syntactic features (syn), with cosine similarity below 50%. Nodes are colour-coded to represent r/newzealand in black, six city-level communities in blue, 14 peripheral communities in red, and remain… view at source ↗
Figure 7.19
Figure 7.19. Figure 7.19: Confusion Matrix from C2xG Classification Model Description: This figure displays a confusion matrix for the New Zealand-related groupings classification model, utilising SEM+ features parsed via C2xG and trained on New Zealand-related communities; all data is sourced from Reddit/Pushshift (Baumgartner et al., 2020). 218 [PITH_FULL_IMAGE:figures/full_fig_p250_7_19.png] view at source ↗
Figure 7.20
Figure 7.20. Figure 7.20: Cosine Similarity of the Hypocoristic Word Pairs [PITH_FULL_IMAGE:figures/full_fig_p256_7_20.png] view at source ↗
Figure 7.21
Figure 7.21. Figure 7.21: Semantic Shift in chippy Description: This figure depicts the semantic shift of chippy across three time periods, including chippy1 (before 2016-11), chippy5 (2021-07 to 2022-01), and chippy10 (after 2024-06), based on data sourced from Reddit/Pushshift (Baumgartner et al., 2020). 232 [PITH_FULL_IMAGE:figures/full_fig_p264_7_21.png] view at source ↗
Figure 7.22
Figure 7.22. Figure 7.22: Semantic Shift in snapper Description: This figure illustrates the semantic shift of snapper over three distinct periods, specifi￾cally snapper1 (before 2016-11), snapper5 (2021-07 to 2022-01), and snapper10 (after 2024-06), with data sourced from Reddit/Pushshift (Baumgartner et al., 2020). 233 [PITH_FULL_IMAGE:figures/full_fig_p265_7_22.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Circularity Check

0 steps flagged

No circularity: empirical corpus analysis and embeddings rest on external data, not self-definition.

full rationale

The paper constructs a 4.26B-word Reddit corpus, performs qualitative perception analysis, and applies standard static/diachronic Word2Vec embeddings to measure semantic variation. No equations, fitted parameters renamed as predictions, or self-citations that bear the central load appear in the provided abstract or described methods. Claims about place-language associations derive from observed patterns in the collected data rather than reducing to inputs by construction. The derivation chain is self-contained against the external corpus and does not invoke uniqueness theorems or ansatzes from prior self-work that would force the result.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the domain assumption that social media language use reliably indexes geographic place identity and dialect alignment; no free parameters, invented entities, or additional axioms are stated in the abstract.

axioms (1)
  • domain assumption Language use in place-informed social media communities reflects geographic dialect alignment and place identity.
    This premise underpins the entire investigation as described in the abstract.

pith-pipeline@v0.9.0 · 5440 in / 1374 out tokens · 68169 ms · 2026-05-10T08:31:52.274803+00:00 · methodology

discussion (0)

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Reference graph

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293 extracted references · 186 canonical work pages · 1 internal anchor

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