Mate preferences matching outcomes online dating brisbane single parent dating site

19-Sep-2019 02:06

The users’ profiles include 35 attributes, such as user ID, gender, birthday, education level, mate requirements and so on.

From the perspective of evolutionary and social psychology [].To address the research gap, in this paper, using empirical data from a large online dating site in China, we explore the users’ attribute preference compared with random selection, and use logistic regression to study how the users’ demographic attributes, popularity and activity and compatibility scores are associated with messaging behaviors, which reveal the gender differences in potential mate selection.We also use ensemble learning classifiers to sort the importance of various potential factors predicting messaging behaviors.For general social networks, gender differences lead to obvious differences in behaviors and preferences between men and women.Research on an online-game society showed that females perform better economically and are less risk-taking than males, and they are also significantly different from males in managing their social networks [].

From the perspective of evolutionary and social psychology [].To address the research gap, in this paper, using empirical data from a large online dating site in China, we explore the users’ attribute preference compared with random selection, and use logistic regression to study how the users’ demographic attributes, popularity and activity and compatibility scores are associated with messaging behaviors, which reveal the gender differences in potential mate selection.We also use ensemble learning classifiers to sort the importance of various potential factors predicting messaging behaviors.For general social networks, gender differences lead to obvious differences in behaviors and preferences between men and women.Research on an online-game society showed that females perform better economically and are less risk-taking than males, and they are also significantly different from males in managing their social networks [].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.