Let’s Play a Love Game: Applying Gamification Components to Online Dating
Bibliographic record
Abstract
The use of digital games and gamification has demonstrable potential to impact aspects of many professional industries, however it also can impact our personal lives, particularly in affecting how we date. This paper examines the use of gamification in dating apps and explores how apps, like Tinder, Bumble, and Hinge provide a game like user experience. It reviews existing literature on the use of games in the dating app sector, seeking to consolidate findings to address research questions regarding dating app perception and identify key dating apps that utilize components of gamification. This study also compares existing dating apps with newly developed dating app, Heartcade. This case study will be used to examine the gamification components. This study will also examine how the mechanics, dynamics, and aesthetics (MDA framework) applies to various dating apps and how their UX design elements impact the user perception and gamify the user experience. It also discusses the theory of procedural rhetoric and the persuasive power of dating apps. This MRP argues that the dominant rhetoric intended by a team of dating app designers is subject to manipulation through user choice. This case study paves the way for future investigation into dating apps and their function as a game. The findings consolidate evidence showing how dating apps that utilize gamification can have impacts in multiple areas of relationship building and finding meaningful relationships. They also highlight the challenges and pitfalls of applying gamification principles and discuss the implications of development and evaluation methodologies on the success of a game-based solution. A dating app prototype is linked that applies the principles of gamification as discovered from my research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".