Glossary

Concise index of the central terms — links to fuller treatment in the main articles. Alphabetical.

A

Algorithmic bias

Systemic distortion in recommender systems from skewed training data, popularity amplification, and (often) explicit filters. → article

Attention economy

The core scarcity on dating platforms is not profile count but attention. Unequally distributed, self-reinforcing feedback. → article

C

Collaborative filtering

Recommendation logic that groups users by similar swipe patterns and draws recommendations from those clusters. Amplifies popularity bias. → mechanics

D

Dwell time

Average time other users spend on your profile. Not derivable from the Hinge export. → KPIs

E

ELO rating

Chess-derived rating system used in early Tinder as a hidden attractiveness score. Officially superseded, residually still in effect. → mechanics

G

Gale-Shapley algorithm

Nobel-prize-winning algorithm for the stable marriage problem; used in Hinge’s “Most Compatible” feature. → mechanics

Ghosting

Sudden, unexplained end of contact after initial conversation. In online dating the default exit, not the exception. → article

Gini coefficient

Inequality measure (0 = perfect equality, 1 = max concentration). Used in dating apps to quantify like distribution. → asymmetries

L

Label fatigue

Exhaustion from social pressure to categorize gender/sexuality. Especially pronounced in younger queer cohorts. → LGBTQIA+

Lorenz curve

Graphical representation of distributional inequality. Used in CupidLeaks to visualize like concentration. → asymmetries

M

MDC — Match-to-Date-Conversion

Share of matches that lead to a real date. In mainstream dating typically 3–10 %. → KPIs

MRR — Message-Response-Rate

Share of first messages that get a reply; primary indicator of conversation quality. → KPIs

P

Paradox of choice

Empirically documented: as the number of options grows, decision capacity and satisfaction with the chosen option decline. → attention economy

Pareto principle

80/20 distribution; on dating apps often exceeded — e.g., 80 % of likes to the top 25 %. → asymmetries

PVS — Profile-Visibility-Score

Subjective first-impression impact (~3 s); not directly derivable from export. → KPIs

R

Reciprocal Recommender System (RRS)

Recommendation architecture that models mutual interest instead of one-sided preference. → mechanics

Romance scam

Organized fraud pattern: weeks of relationship building, then financial “emergency”. $1.14 bn US losses in 2023. → safety

S

SMR — Swipe-to-Match-Ratio

matches ÷ likes. Primary indicator of market value; sharply gender-dependent. → KPIs

Surveillance pricing

Hyper-personalized pricing based on demographics and surveilled data; same product, different prices for different users. → monetization

Swipe fatigue

Emotional and physical exhaustion from repetitive swiping; affects 79–80 % of Gen Z and millennials. → attention economy