Gender-specific preference in online dating

Abstract

In this paper, to reveal the differences of gender-specific preference and the factors affecting potential mate choice in online dating, we analyze the users' behavioral data of a large online dating site in China. We find that for women, network measures of popularity and activity of the men they contact are significantly positively associated with their messaging behaviors, while for men only the network measures of popularity of the women they contact are significantly positively associated with their messaging behaviors. Secondly, when women send messages to men, they pay attention to not only whether men's attributes meet their own requirements for mate choice, but also whether their own attributes meet men's requirements, while when men send messages to women, they only pay attention to whether women's attributes meet their own requirements. Thirdly, compared with men, women attach great importance to the socioeconomic status of potential partners and their own socioeconomic status will affect their enthusiasm for interaction with potential mates. Further, we use the ensemble learning classification methods to rank the importance of factors predicting messaging behaviors, and find that the centrality indices of users are the most important factors. Finally, by correlation analysis we find that men and women show different strategic behaviors when sending messages. Compared with men, for women sending messages, there is a stronger positive correlation between the centrality indices of women and men, and more women tend to send messages to people more popular than themselves. These results have implications for understanding gender-specific preference in online dating further and designing better recommendation engines for potential dates. The research also suggests new avenues for data-driven research on stable matching and strategic behavior combined with game theory.

The World of Images in Dating Apps: “Dictatorship of Clichés” vs. Naturality

“…İlk çalışmaların 1990'larda yapılmaya başlandığı ama Tinder'ın popülaritesi ve flört uygulamalarının kullanımının yaygınlaşmasıyla âdeta bir patlama yaşayan çevrimiçi flört literatürü, beş temel konuyu incelemektedir : a) bireylerin flört uygulamalarını kullanma motivasyonları (Alam vd., 2018;Bryant ve 4 Sheldon, 2017;Newett vd., 2017;Pozsar vd., 2018;Ranzini ve Lutz, 2017;Sumter vd., 2017), b) kullanıcıların eş seçimleri (Başar, 2010;Felmlee ve Kreager, 2017;Hitsch vd., 2010;McGloin ve Denes, 2018;Peters ve Salzsieder, 2018;Su ve Hu, 2019), c) benlik sunumları (Davis ve Fingerman, 2016;Duguay, 2017;Gewirtz-Meydan ve Ayalon, 2018;Tanrıöver ve Sunam, 2017), d) bu platformların ilişkilere, flört kültürü ve deneyimlerine etkileri (Albury vd., 2017;Aydoğan, 2020;Deniz, 2020;Illouz, 2019;Krüger ve Spilde, 2018;Vuzharov, 2019), e) flört teknolojilerinin bireyler üzerindeki olumsuz psikolojik etkileri (Hess ve Flores, 2018;Paasonen vd., 2019;Shaw, 2016;Whitty, 2015;Zerach, 2016).…”

Identity Health

“…Many adolescents frequently hesitate to lose their free time and conformity, and it simultaneously means that they hate any interruptions by others and physical contacts. As necessary, they can choose tentative friends online (Su and Hu 2019). Using smartphone, many matching service applications for marriage among future partners have been launched in Japan.…”

Reciprocal Recommender Systems: Analysis of State-of-Art Literature, Challenges and Opportunities towards Social Recommendation

“…The temporal behavior and preferences of users in dating sites were analyzed in [160] based on messaging and replying patterns, discovering among other facts that males tend to seek younger females and females prioritize socio-economic status or education level of male users. Another study [147] focuses on gender attribute differences in the processes of selecting a potential partner, showing that female users tend to be the only ones who consider how a male user suits their preferences, and what his requirements would in turn be like. From a sociological perspective, in [53] graph data about message exchanges are mined to identify clusters of users who tend to interact with each other in dating sites.…”