Online communities and dating apps: The effects of social presence, trust, and Covid-19
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
The Covid-19 pandemic changed the dynamics of socialization by restricting one of its main avenues: in-person gatherings. This pushed people towards digital technology to fulfill their socialization needs. In this paper, we take steps to explore whether features of online communities can contribute to innovative dating app designs, given how dating app business models currently focus on independent one-on-one interactions. We conducted an exploratory survey of 200 participants concerning dating app use habits, perceptions of dating apps, as well as degrees of trust, social presence, and perceived ease of finding dates using three dating methods. We found that social presence and trust consistently predicted the perceived ease of finding dates for each method, and that the perceived ease of finding dates influenced whether participants reported increased use of the method during Covid-19. Together with the growth in online community participation, these results suggest that dating app platforms might benefit from incorporating social features in their designs. © AMCIS 2021.
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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 | 0.000 | 0.001 |
| 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