Mapping the Distribution and Spread of Social Ties Over Time: A Case Study Using Facebook Friends
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Relational geography asserts that social networks provide geographic benefits, and geographies are transmitted through the sharing of local knowledge and experience. To articulate the spatial expanse and geographic benefits of an individual’s social network, researchers require better social-spatial geographic information system models illustrating how contacts are dispersed, and how many distinct places they inhabit. In this work, the authors conduct a case study to map social network ties in geographic space. The authors retrieve social network matrices for 20 volunteers (egos) via Facebook.com, amounting to over 8,500 friends (alters). Each ego listed the alter’s hometown city at two time periods: at relationship inception and at the time of the study. The authors measure specific tie locations, tie expanse, deviation from a gravity model prediction, and expansion of alter groups (family, clubs, neighbors, etc.) over time. The authors find that social networks geographically spread over time, on average, from 2,679 km (standard distance) to 3,258 km (standard distance), and that the average ego had alters in 21 unique locations when they met, and 38 locations at the time of the study. Regarding friend groups, the authors discover that high school friends and friends from non-residential gatherings (ex. conferences) dispersed the most (over 1,900 km), and cultural groups (churches, sports teams) and family dispersed the least (less than 800 km) over time. Our results lead to a discussion of how mapping and measuring the distribution of social connections can uncover changing dynamics of social interaction, and one’s ability to access and engage with places through social ties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it