Stereotypical love: a cluster analysis of self-presentation strategies in tinder profile pictures.

Abstract

Background: Although dating apps are the preferred means of meeting sexual and romantic partners, users frequently experience disappointment, highlighting the importance of understanding self-presentation strategies and selection processes to mitigate negative experiences.

Aim: This study examines self-presentation strategies in Tinder profile pictures, aiming to identify typological patterns, characterize common profile-building strategies, and analyze differences by age, gender, and sexual orientation.

Methods: We employed a mixed dimensional approach-both categorical and numerical-to characterize and categorize 1000 Tinder profile pictures. A descriptive category set was developed to analyze key picture elements, and a not-safe-for-work nudity index was computed using an open-source neural network. We then applied K-means clustering to identify patterns in the data.

Outcomes: The main outcome measures included the clustering distribution of profile picture types and their associations with demographic variables (Standardized Pearson Residuals).

Results: Our analysis identified nine prototypical Tinder profile image categories, confirming the presence of stereotypical patterns in self-presentation. Additionally, we found statistically significant associations between profile clustering and user demographics, particularly age, gender, and sexual orientation.

Clinical implications: Stereotyped self-disclosure in dating apps may hinder partner selection by reinforcing social biases related to age, gender, and sexual orientation, with potential consequences for sexual and couple therapy.

Strengths & limitations: Key strengths include the use of a large and diverse dataset, robust cluster validation techniques, and a novel approach to analyzing self-presentation on dating apps. Limitations, however, include potential biases due to the categorical nature of the data, difficulties in capturing individual nuances in self-presentation, and the inability to account for Tinder algorithm influences on image use.

Conclusion: Clustering techniques provide an empirical framework for identifying stereotypical self-presentation patterns and demographic differences, which could be extended to analyzing written descriptions and partner selection mechanisms.