Swiping for the right reasons: Approach and avoidance goals are associated with actual and perceived dating success on Tinder
Bibliographic record
Abstract
Tinder, the mobile dating app, is widely used for meeting potential dating partners, but little research has investigated the dating experiences of users. In two studies, we applied the approach-avoidance theory of social motivation to understand the association between people’s goals for Tinder use and their perceived and actual dating success. In Study 1 we found that higher approach goals for using Tinder, such as to develop intimate relationships, were associated with more positive beliefs about people on Tinder, and in turn, associated with reporting greater perceived dating success, initiating more conversations on Tinder, and going on more second dates with people from Tinder. In contrast, people who had higher avoidance goals when using Tinder, such as aiming to avoid embarrassment, reported feeling more anxious when using Tinder and in turn, perceived less dating success and reported fewer second dates. In Study 2—a preregistered replication of Study 1—we largely replicated the effects from Study 1. Additional analyses in both studies revealed that the results were not accounted for by attractiveness of the user and were consistent between men and women, but differed based on the age of the user. The associations between approach goals and dating success were stronger for younger, compared to older users and the association between avoidance goals and dating success were stronger for older, compared to younger, users. The findings have implications for understanding the role of motivation in dating success on Tinder and reveal novel mechanisms for the associations between dating goals and dating success.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".