The echoes of social media friends’ travels: social influence and venue selection in a hyperconnected world
Bibliographic record
Abstract
This research examines how social media friends influence each other’s travel decisions by investigating the roles of geographic distance and venue type. Through analysis of over 22 million check-ins from 112,000 users across Foursquare and Twitter platforms, we provide unprecedented empirical evidence of how social influence manifests in actual travel behaviors. Our findings reveal two key patterns: social influence diminishes systematically with distance, with friends showing 12% venue overlap for destinations at least 50 km from home, decreasing to 5% at 10,000 km; and influence varies meaningfully across venue categories, with Travel and Transport venues demonstrating the strongest friend overlap. These results extend both social comparison theory and construal level theory by providing large-scale empirical validation of how psychological distance affects social influence in digital travel behavior. This study offers valuable insights for developing personalized travel recommendations and social network-based marketing strategies.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".