Digital Deception in the Online Dating Space: A Study of Tinder
Bibliographic record
Abstract
As technology continues to impart its worldview, the role of communication in the navigation of dating in online spaces has also evolved. This study examines the relationship between communication and digital deception within a selected population of Tinder users. Tinder is a geo-social, location-aware dating application that is used by millions of people around the world. There are three fundamentally specific objectives of this research, which include: first, examining the ways in which dating apps increase the possibility of digital deception; second, exploring ways in which Tinder's design and functionality contribute to the occurrence of digital deception; and finally, identifying and examining the impacts of online deception, particularly in the context of dating apps, on human communication and relationship formation. To obtain first-hand perceptions of online representation and digital deception on Tinder (and as with other online social platforms), 51 Tinder users from Nigeria and Canada were surveyed through their responses to a questionnaire distributed on June 20 and July 11, 2023. The findings of this study suggest that the use of dating apps among youths has increased, leading to prevalent lying and distrust. In the context of using Tinder among the sampled population, Tinder's design, functionality, and online communication in general facilitate and contribute to instances of digital deception, as its affordances only give room to do little, hence, there is often an attempt to ‘put best foot forward’ and the tendency of lying becomes imminent. Appearance influences deception, but some still trust online dating for meaningful connections; platforms should promote honesty.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".