Constructing a Sense of Place in Social Media
Bibliographic record
Abstract
While social media platforms are often perceived as placeless, recent research into social media platforms have identified place identities of a similar nature to real-world communities. Of interest to social scientists is how participants on social media platforms construct these place identities, also known as a sense of place, solely through their behaviour when spatial connections are lacking. In our investigation, we focus on one form of behaviour: language-use associated with a place (i.e., dialect). Our primary research question is how participants construct a sense of place on social media platforms through language-use. We took crowd-sourced dialect terms from Wiktionary for six inner-circle national varieties of English (Australia, Canada, Ireland, New Zealand, United Kingdom, and the United States). We then compared the distribution of these dialect terms on subreddits associated with these. Additionally, we analyse the distribution of these dialect terms associated with New Zealand English for six cities in New Zealand (Auckland, Hamilton, Tauranga, Wellington, Christchurch, and Dunedin). Our hypothesis is that if these place-based subreddits maintain a sense of place, then there should be a relationship between the presence of these dialect terms and their respective places. We take approaches from Natural Language Processing (NLP) to process and analyse our data. Our results show that there is a relationship between these dialect terms at both country-level and city-level geographies. Therefore, we can reject the null hypothesis that there is no relationship and that dialect terms partially contribute to the sense of place on social media platforms. Additionally, we show that the distribution of these dialect terms correlate with other demographic patterns when we combine our analysis with census and survey data. The findings from our study suggests that language-use can be used as a measure of place identity in the absence of other sociogeographic information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".