Presenting Your Best Self(ie): The Influence of Gender on Vertical Orientation of Selfies on Tinder
Bibliographic record
Abstract
When taking a self-portrait or "selfie" to display in an online dating profile, individuals may intuitively manipulate the vertical camera angle to embody how they want to be perceived by the opposite sex. Concepts from evolutionary psychology and grounded cognition suggest that this manipulation can provide cues of physical height and impressions of power to the viewer which are qualities found to influence mate-selection. We predicted that men would orient selfies more often from below to appear taller (i.e., more powerful) than the viewer, and women, from an above perspective to appear shorter (i.e., less powerful). A content analysis was conducted which coded the vertical orientation of 557 selfies from profile pictures on the popular mobile dating application, Tinder. In general, selfies were commonly used by both men (54%) and women (90%). Consistent with our predictions, a gender difference emerged; men's selfies were angled significantly more often from below, whereas women's were angled more often from above. Our findings suggest that selfies presented in a mate-attraction context are intuitively or perhaps consciously selected to adhere to ideal mate qualities. Further discussion proposes that biological or individual differences may also facilitate vertical compositions of selfies.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".