Gender-specific preference in online dating

In online dating, there are significant gender differences in terms of attribute preference, self-presentation and interaction [47]. Users usually have a certain preference for mates’ age or height. For both men and women, when they send messages to their potential partners, we compute the age difference as age(receiver) − age(sender), and the height difference as height(receiver) − height(sender). Figures 1 and 2 show the age difference and height difference distributions, respectively. As a comparison, we also show the randomized results by assuming that female(male) users randomly send messages to male(female) users.

In most times and places, women usually marry older men [48, 49]. Figure 1 shows that in modern Chinese society, on average, men prefer women two years younger than them and women prefer men two years older than them. However, the range of age difference that women accept is smaller than that of men: the minimum age women accept is that men are 11 years younger than them and the maximum age they accept is that men are 23 years older than them, while the minimum age men accept is that women are 25 years younger than them and the maximum age they accept is that women are 28 years older than them. If only the age difference distributions are considered, in line with previous findings from a range of cultures and religions [50], we find that the range of ages that women are willing to message is narrower than the range of ages that men are willing to message. Male and female preferences are not random; they seek potential dates with a smaller age difference than predicted by random selection, which shows the characteristic of likes-attract.

Figure 2 shows that generally the height difference for women sending messages to men (most are 12 cm) are larger than that for men sending messages to women (most are 10 cm) when choosing potential mates. In China, for men, the ideal height difference is that they are 10 cm taller than the person they message, while for women, the ideal height difference is that they are 12 cm shorter than the person they message. According to the data from Yahoo! dating personal advertisements, for users in the U.S., height also matters for dating, especially for females [51]. In Fig. 2, the height difference range for women is smaller than that for men: the minimum height women accept is that men are 3 cm shorter than them and the maximum height they accept is that men are 30 cm taller than them, while the minimum height men accept is that women are 13 cm shorter than them and the maximum height they accept is that women are 32 cm taller than them. Females show the characteristic of likes-attract in terms of preference for height. As is same with age, users seek potential mates with a smaller height difference than predicted by random selection, although the difference is not as obvious as age difference.

It is noteworthy that in the dating site, users’ characteristics are all self-reported. For impression management considerations [52], users can exaggerate their personal characteristics [53]. For example, a recent research on online self-reported height against objectively measured data in young Australian adults revealed that self-reported height is significantly overestimated by a mean of 1.79 cm for males and 1.29 cm for females [54]. Men lie more than women about their height, which is also found in the online daters of New York City [55]. We note that users seem to have not accurately reported their physical height in the dating site. In the dataset, the average heights of female and male users are 161.99 cm (\(\mathit{SD}=4.18\)) and 173.08 cm (\(\mathit{SD}=4.68\)), respectively. However, in real world the average heights of adult females and males in China are 160.88 cm and 169.00 cm, respectively, which means that female and male users can exaggerate their height by an average of 1.11 cm and 4.08 cm, respectively. After correcting these, we find that real height differences \(10-(4.08-1.11) = 7.03\text{ cm}\) for men, and \(12-(4.08-1.11) = 9.03\text{ cm}\) for women would be significant. However we also notice that in the dating site, the average ages of male and female users are 28.73 and 28.58 years old, respectively, while in the overall adult population in China, the average ages of men and women are 40.56 and 41.01 years old respectively according to the population census data. The dating population is younger than the overall adult population, thus is likely taller, and users may not exaggerate their height by quite as much as calculated.

When a user sends a message to another user, his/her choice of recipient may not be random, but rather has some preference for certain attributes, such as preference for employment, education, income, and so on. To characterize the preference of sender with attribute i for receiver with attribute j, let \(m_{ij}\) be the number of messages sent from users with attribute i to users with attribute j, \(m_{i}\) be the total number of messages sent from users with attribute i, \(n_{j}\) be the number of receivers with attribute j, and n be the total number of receivers, then the attribute preference is \(p_{ij} = m_{ij} /m_{i} - n_{j} /n\). \(p_{ij}>0\) indicates that compared with random selection, senders with attribute i have a preference for receivers with attribute j, \(p_{ij}=0\) indicates that there is no preference and \(p_{ij}<0\) indicates negative preference, i.e. preferring not to select the receivers with attribute j.

Employment preferences are shown in Figs. 3 and 4 (see Tables 1 and 2 in Additional file 1 for the meanings of attributes and the number and proportion of men/women for each employment). We find that compared with males sending messages to females, when female users send messages to male users, there is a stronger preference for the employments of their potential mates. In Fig. 3, we find that women who are students, accountants, educators or in other uncategorized occupations are not preferred by men, while women engaged in design are slightly popular in terms of the relative amount of messages received, especially for men in aviation service industry. At the same time, we also find that in these data, men engaged in housekeeping only send messages to women in accounting and men engaged in translation industry only send messages to women who are private owners, which may be due to the small sample size of user behavior with respect to these attributes.

From Fig. 4, we find that the most popular professions for men are senior management, finance, education and private owners. Most people in these four occupations have high income or are well-educated. Unpopular male users are school students, salesmen and those engaged in other uncategorized occupations. At the same time, women engaged in chemical industry tend to seek men engaged in education and training, women engaged in sports tend to seek men who are private owners, and women engaged in police only send messages to men engaged in finance and real estate in these data, which may also be attributed to the small sample size of user behavior with respect to these attributes.

Education levels have a significant impact on mating and marriage [22]. Education level preferences are shown in Figs. 5 and 6 (see Tables 3 and 4 in Additional file 1 for the meanings of attributes and the number and proportion of men/women for each education level). In China, like in the other countries, postdoctor also refers to a position rather than an educational achievement. However, in many Chinese websites, when a user registers, postdoctor is also considered an education level beyond obtaining a PhD. Similarly we find that compared with males sending messages to females, when female users send messages to male users, there is a stronger preference for the education level of their potential mates. Figure 5 shows that men whose education level is below the undergraduate degree tend to look for women the same academic qualifications as them or lower than their qualifications, men with education level higher than bachelor degree but lower than doctoral degree tend to look for women with bachelor degree, and men with a PhD degree or postdoctoral training tend to look for women with graduate degree. In terms of preference for education levels, generally men show likes-attract characteristic. For female users sending messages to male users, Fig. 6 shows that men with undergraduate and graduate degrees are popular and, for most women, undergraduate males are more popular, but graduate females are more likely to seek potential mates with graduate degree. In terms of preference for education levels, generally women show potentials-attract characteristic. Research on a German online dating site revealed that preference for similar educational background increases with educational level. Females are reluctant to communicate with males with lower educational levels, however there are no barriers for males to contact females with lower educational qualifications [22].

Education level and income are two important indicators of a person’s social and economic status. From Figs. 7 and 8 (see Tables 5 and 6 in Additional file 1 for the meanings of attributes and the number and proportion of men/women for each income level) we find that, in terms of income levels, there is less obvious preference on potential mate selection for male users compared with female ones. On the one hand, as shown in Fig. 7, all men obviously prefer women whose monthly income is between RMB 5000 and RMB 10,000 (the RMB is the Chinese currency, and RMB 1 = 0.145 US Dollars = 0.128 Euros), while women whose income is below RMB 2000 are obviously excluded. However, men show no obvious preference or exclusion for women whose income is above RMB 10,000. On the other hand, as shown in Fig. 8, all women dislike men who earn less than RMB 5000, and men who earn RMB 10,000 to RMB 20,000 are the most popular. In terms of preference for income levels, generally women also show potentials-attract characteristic. A field experiment on a Chinese online dating site found that men visited the profiles of women of different incomes with roughly the same rates, while for women, the higher the male incomes are, the greater the rates of visiting their profiles will be [38], which is different from our findings.

On users’ personal homepages, each user has shown the demands to the potential mates, including requirements for 7 attributes, i.e. age, avatar, education level, height, credit rating, place of residence and marital status (see Figs. 1–4 in Additional file 1 for the selection requirements of several attributes). As for credit rating, on the dating site, after a user passes the quick identity authentication, or uploads one of three documents (the ID card, the passport or the Hong Kong and Macau Pass) and passes the review, he/she will obtain the first star, i.e. credit rating equals 1. On the basis of the first star, each time a new document is uploaded and approved, an additional star or rating can be added (up to five stars, i.e. five-star member). Besides although on the platform the minimum age of users is 18, there are still very few users who set their requirement for minimum or maximum age below 18 (see Fig. 3 in Additional file 1 for details). We apply the concept of compatibility score to describe the match between users based on whether or not a user meets another user’s selection requirement. When women send messages to men, for each message and for each attribute, we can obtain the proportion of women who match the mate preferences of men and the proportion of men who meet the preferences of women, i.e. we can get two vectors including 7 proportions. According to the data we obtain \(\mathbf{w}_{\mathrm{FMm}}= (0.701,0.886,0.462,0.826,0.919,0.786,0.920)\), and \(\mathbf{w}_{\mathrm{FMf}}=(0.912,0.976,0.681,0.962,0.994,0.864,0.912)\), where \(\mathbf{w}_{\mathrm{FMm}}\) is the proportions of female attributes meeting male preferences and \(\mathbf{w}_{\mathrm{FMf}}\) is the proportions of male attributes consistent with female preferences. Similarly when men send messages to women, we obtain \(\mathbf{w}_{\mathrm{MFm}}=(0.877,0.977,0.402,0.980,0.992,0.831,0.960)\) and \(\mathbf{w}_{\mathrm{MFf}}=(0.671,0.867,0.572,0.678,0.758,0.771,0.892)\). Thus the compatibility scores of women sending messages to men are and the compatibility scores of men sending messages to women are where (female attr. in male pref.) is a vector characterizing whether female attributes meet male preferences for a pair of users (1 for yes and 0 for no), and similarly (male attr. in female pref.) is a vector characterizing whether male attributes meet female preferences for a pair of users. Equations 1 and 3 are the compatibility scores between a male preference and the profile of his chosen mate, and Eqs. 2 and 4 are the compatibility scores between a female preference and the profile of her chosen mate. For a pair of users, \(u_{a}\) and \(u_{b}\), we use a score, i.e. reciprocal score, to quantify how much the attributes of \(u_{b}\) match the preferences of \(u_{a}\) and how much the attributes of \(u_{a}\) match the preferences of \(u_{b}\). The reciprocal score between \(u_{a}\) and \(u_{b}\) is the mean of the compatibility scores of these two users, that is, for women sending messages to men the reciprocal score is \(\mathit{rs} = (c_{\mathrm {FMm}} + c_{\mathrm{FMf}} )/2\), and for men sending messages to women \(\mathit{rs} = (c_{\mathrm{MFm}} + c_{\mathrm{MFf}} )/2\).

Let click be the number of times a user is clicked, msg be the number of messages received by a user, and rec be the number of times a user is recommended and shown on the other users’ homepages, we define \(\mathit{pop}_{1} = \mathit{click}/\mathit{rec}\) and \(\mathit{pop}_{2} = \mathit{msg}/\mathit{rec}\) which can characterize the popularity of a user based on actions. We also use PageRank centrality (\(\mathit{pop}_{3}\)) to quantify how focal or popular a user is in a network by considering all connections in the network. Attractive people, such as the people with advantageous demographic attributes and higher socio-economic status, tend to be more demanding than average people in terms of potential mate choice, which can be revealed in the preference analysis of income and education level in Sect. 3.1.2. Those who are perceived as attractive by attractive people can be even more popular/attractive. The variables used in the paper and their meanings are shown in Table 1.

We introduce several centrality indices, such as \(\mathit{pop}_{1}\), \(\mathit{pop}_{2}\), \(\mathit{pop}_{3}\), and indegree, to evaluate their correlation with messaging behaviors. It is noteworthy that the centrality indices are aggregated indicators describing users’ desirability or popularity, and users do not know their indices, nor do they know the indices of others. We use outdegree to characterize users’ activity level, and in the dating site, users also do not know the outdegree of other users. In reality, instead of using the indices to identify or select attractive partners, users will message another based on more specific clues, such as higher income, better education background, attractive photos or good demographic and socio-economic compatibility. In the paper, we will evaluate whether the indices are significantly associated with messaging behaviors.

Suppose \(p_{i}\) is the probability of sending messages for a female user i, \(1-p_{i}\) is the probability of not sending messages, then \(L_{f_{i}}=\ln(\frac{p_{i}}{1-p_{i}})\), i.e., for all women, \(L_{f}=\ln(\frac{p}{1-p})\). Similarly, suppose \(q_{j}\) is the probability of sending messages for a male user i, \(1-q_{j}\) is the probability of not sending messages, then \(L_{m_{j}}=\ln (\frac{q_{j}}{1-q_{j}})\), i.e., for all males, \(L_{m}= \ln(\frac{q}{1-q})\). We obtain logistic regression models as follows:

In this study, multicollinearity tests are conducted to find out independent variables among which the correlation coefficients are less than 0.5 (see Tables 7 and 8 in Additional file 1 for details). The logistic regression results for women sending messages to men are shown in Table 2. We find that almost all the variables are significant when only considering the attributes of women (model 1), i.e., the attributes of senders, but only housing and outdegree of women are positively associated with the probability of women sending messages to men. When only considering the male attributes (model 2), except male mobile phone verification and credit rating, all the others are significant and are positively associated with the probability of women’s sending messages. When considering the two parties’ attributes and compatibility scores (model 3), among the significant variables, female mobile phone verification, car ownership, credit rating and popularity levels (\(\mathit{pop}_{1}\) and \(\mathit{pop}_{3}\)) are negatively associated with the probability of women’s sending messages, while the other variables are positively associated. We find that, when women send messages to men, they are concerned about not only whether they meet the requirements of men but also whether men meet their own requirements.

The logistic regression results for men sending messages to women are shown in Table 3. We find that when only the female attributes are considered (model 1), except female mobile phone verification, credit rating and outdegree, all the other variables are significant, but only female house ownership affects probability of male messaging in a negative way. When only male attributes are considered (model 2), all the variables are significant but only male outdegree is positively correlated with messaging behaviors, others negatively correlated. With all variables considered (model 3), except for female credit rating, outdegree, and the compatibility score between a female preference and the profile of the corresponding other side, all other variables are significant. Among the significant variables, female mobile phone verification, car ownership, popularity (\(\mathit{pop}_{1}\), \(\mathit{pop}_{2}\) and \(\mathit{pop}_{3}\)), male outdegree and the compatibility score between a male preference and the profile of the corresponding other side are positively correlated with messaging behaviors, while all the other variables are negatively correlated. In addition, by analyzing the significance of the two compatibility scores, we find that men only pay attention to whether women meet their own requirements when sending messages to women.

As can be seen from Tables 2 and 3, for males or females sending messages, popularity of the other side is significantly positively associated with messaging behaviors. On the one hand, \(\mathit{pop}_{1}\) and \(\mathit{pop}_{2}\) values, according to their calculation method, represent a user’s local popularity. On the other hand, \(\mathit{pop}_{3}\) value, i.e. PageRank, represents the popularity of a user from a global perspective.

For females sending messages to males, \(\exp (0.390) = 1.477\) for male \(\mathit{pop}_{1}\) is larger than \(\exp (0.146) = 1.157\) for male \(\mathit{pop}_{3}\), and for males sending messages to females, \(\exp (0.462) = 1.587\) for female \(\mathit{pop}_{1}\) is also larger than \(\exp (0.141) = 1.151\) for female \(\mathit{pop}_{3}\). Thus, for both males and females, the other party’s \(\mathit{pop}_{1}\) is more important than \(\mathit{pop}_{3}\). Besides we also find that, when females send messages to males, \(\exp (0.390) = 1.477\) for male \(\mathit{pop}_{1}\) is less than \(\exp (0.462) = 1.587\) for female \(\mathit{pop}_{1}\) when males send messages to females, which indicates that compared with females, for males the other side’s \(\mathit{pop}_{1}\) is more associated with their messaging behaviors. However, when females send messages to males, \(\exp (0.146) = 1.157\) for male \(\mathit{pop}_{3}\) is larger than \(\exp (0.141) = 1.151\) for female \(\mathit{pop}_{3}\) when males send messages to females, which indicates that compared with males, for females the other side’s \(\mathit{pop}_{3}\) is more associated with their messaging behaviors.

In China, having an apartment and a car is a symbol of a person’s wealth and social status, and in some regions, they have become necessities for getting married. When women send messages to men, it is important for men to have a house and a car. When men send messages to women, it is not important for women to have a house but it’s somewhat important for women to have a car. We find that \(\exp(0.038) = 1.039\) for whether the other side has a car when men send messages to women is smaller than \(\exp (0.157) = 1.170\) for whether the other side has a car when women send messages to men, indicating that women pay more attention than men to whether the other side has a car.

A user’s outdegree quantifies the user’s activity. Seemingly high activity means contacting many other users, however, essentially it may imply that users invest more time and resources in attempting to find potential partners. Outdegree is an attribute different for men and women. When a woman sends a message to a man, the other side’s outdegree is significantly positively associated with the messaging behavior, while not when a man sends a message to a woman. When women send messages to men, network measures of popularity and activity of the men they contact are significantly positively associated with their messaging behaviors, but when men send messages to women, only the network measures of popularity of the women they contact are significantly positively associated with their messaging behaviors.