Estimating the Effect Of Exercising On Users’ Online Behavior
Bibliographic record
Abstract
This study aims to estimate the influence of offline activity on users’ online behavior, relying on a matching method to reduce the effect of confounding variables. We analyze activities of 850 users who are active on both Twitter and Foursquare social networks. Users’ offline activity is extracted from Foursquare posts and users’ online behavior is extracted from Twitter posts. Users’ interests, representing their online behavior, are extracted with regards to a set of topics in several subsequent time intervals. The shift of users’ interests across different time intervals is taken as a measure of user behavior change on the social network. On the other hand, we employ user check-ins at a gym or fitness center as a sign of exercise and consider it to be an offline activity. In order to find the effect of exercise on online behavior, we identify users who did not go to the gym for at least two months but did so at least nine times in the next three months. We show that shift in interest reduces significantly for users after they start exercising, which implies that the offline activity of exercising can influence how users’ interests are shaped and change on the social network over time.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| 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; a candidate call from one teacher head, 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".