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Online dating body preferences

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 · Hair preferences (particularly color but also hair length) can be a big one. Some people prefer blondes but more men prefer brunettes on average. Similarly, with hair length, If someone says their ideal body type is 'athletic and muscular'. Single Peeps! Join. Submit your health and fitness tips for the chance to be featured in our New Year’s plan! Online  · Hi. I hate this part about selecting my body part. I am not morbidly obese anymore (YAY!) thanks to lapband and am just overweight but still have at least 50 more pounds to go  · Although the research on mate choice, both offline and online, has been extended to many fields, the following problems still exist: (i) online dating sites are a special kind of  · Using data from speed dating events, Eastwick et al. find that among whites, the relative preference for a white partner over a black partner is stronger for conservative than ... read more

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. Employment preference for male users sending messages to female users.

The vertical axis indicates the male occupations and the horizontal axis indicates the female occupations. Preference values are represented by different colors. Employment preference for female users sending messages to male users. The vertical axis indicates the female occupations and the horizontal axis indicates the male occupations. From Fig.

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. 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. 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 preference for male users sending messages to female users. The vertical axis indicates the male education levels and the horizontal axis indicates the female education levels. Education level preference for female users sending messages to male users. The vertical axis indicates the female education levels and the horizontal axis indicates the male education levels.

Postdoctoral females did not send any message to men in the dataset, and we set the elements in the corresponding row to 0. From Figs. On the one hand, as shown in Fig. However, men show no obvious preference or exclusion for women whose income is above RMB 10, On the other hand, as shown in Fig. 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.

Preference for monthly income levels for male users sending messages to female users. The vertical axis indicates the male income levels and the horizontal axis indicates the female income levels. Preference for monthly income levels for female users sending messages to male users. The vertical axis indicates the female income levels and the horizontal axis indicates the male income levels. age, avatar, education level, height, credit rating, place of residence and marital status see Figs.

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. 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.

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. we can get two vectors including 7 proportions. Thus the compatibility scores of women sending messages to men 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.

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.

The variables used in the paper and their meanings are shown in Table 1. 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. 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.

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. 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.

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.

PageRank, represents the popularity of a user from a global perspective. When women send messages to men, it is important for men to have a house and a car. 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 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.

With the advent of the big data era, ensemble learning classification methods have gradually been introduced into the field of social network research. As early as , Breiman proposed the method of bagging [ 56 ], and five years later, he further proposed the method of Random Forest [ 57 ]. Freund proposed the AdaBoost method in [ 58 ], and with the continuous improvement of machine learning classifiers, in , Chen et al.

proposed a classifier—XGBoost [ 59 ], which can greatly improve the efficiency and accuracy of algorithm in some cases. As an application, recently Reece et al. have already applied machine learning tools to identify depression from Instagram photos [ 60 ]. Regression analysis often has certain requirements on the independent variables, such as the absence of multicollinearity, however ensemble learning classification methods relax the constraints on independent variables.

In this section, ensemble learning classification methods including bagging, Random Forest, AdaBoost and XGBoost are used to evaluate the importance of each attribute in Table 1. The numbers of sending and not sending messages are unbalanced in the dataset, and the larger set is subsampled randomly to obtain a set the same size as the smaller one.

The error rates of four ensemble learning classification methods are shown in Table 4. We find that the error rates of Random Forest and AdaBoost are the lowest for females sending messages to males while XGBoost is the lowest for males sending messages to females. Attribute importance ranking is shown in Figs. Similarly, Fig. Attribute relative importance rankings when women send messages to men for different classification methods.

The horizontal axis indicates the attributes and the vertical axis indicates their corresponding importance. For bagging, Random Forest, and AdaBoost, the relative importance of each variable in the classification task is measured by the Gini index, and for XGBoost the relative importance is measured by the Gain parameter.

Attribute relative importance rankings when men send messages to women for different classification methods. The purpose of ensemble learning classification is different from logistic regression analysis. According to Figs. The concept of strategic behavior [ 61 ] derives from economics, where the original implication is that firms take action that affects the market environment to increase profits referring to the message response rate in this study , which is then extended to matching problems [ 35 ], such as mate matching.

Since without user response data, we would like to use centrality indices characterizing user popularity to analyze whether users tend to send messages to people who are more popular than themselves or to those who are less popular. As shown in Tables 5 and 6 , We find that in the dating site men and women show different behavior patterns in messaging despite the reduced cost of rejection in the network environment.

For males sending messages to females, there exist weak positive correlations between centrality indices, which can be characterized by small positive and significant correlation coefficients, while for females sending messages to males, there exist weak or modest positive correlations between centrality indices characterized by small or slightly larger positive and significant correlation coefficients. Men do not show strategic behavior to a large extent when sending messages, while for women, as their centrality indices increase, the corresponding indices of men who received their messages could also increase.

By studying the correlations between the same centrality index pairs for users, we further analyze whether users tend to send messages to people who are more popular than themselves or to those who are less popular. As a comparison, we also give the randomized results. Compared with men, more women tend to send messages to people who are more popular than themselves. Some studies have found a significant positive correlation between the popularity of male and female users.

For example, the research by Taylor et al. on the users from the U. showed that, they tend to select and be selected by other users whose relative popularity is similar to their own, although it does not necessarily mean a higher success rate, i. receiving more responses [ 62 ]. A recent empirical analysis of users in four U. For example, the research on users in Boston and San Diego did not find evidence of strategic behavior [ 33 , 34 ]. Another research on online dating data from a midsized southwestern city in the U.

We find that users on different platforms or in different cultural contexts have different strategic behaviors, and the underlying mechanisms still need to be explored further. In summary, we analyze online dating data to reveal the differences of choice preference between men and women and the important factors affecting potential mate choice. When considering centrality indices, we find that for women, the popularity and activity of the men they contact are significantly positively associated with their messaging behaviors, while for men only the popularity of the women they contact are significantly positively associated with their messaging behaviors.

At the same time, we also find that compared with men, women attach greater importance to the socio-economic status of potential partners and their own socio-economic status will affect their enthusiasm for interaction with potential mates. The machine learning classification methods are used to find the important factors predicting messaging behaviors.

At last strategic behavior is analyzed and we find that there are different strategic behaviors for men and women. Although users do not know the centrality indices of themselves and their potential partners, compared with men, for women sending messages there is a stronger positive correlation between the centrality indices of women and men, and more women are inclined to send messages to people more popular than themselves.

This paper provides a foundation for gender-specific preference of potential mate choice in online dating. On the one hand, this study can provide references for the online dating sites to design better recommendation systems. On the other hand, an in-depth understanding of mate preference, such as the compatibility scores, can help users to select the most appropriate and reliable mates. There are still some limitations for the paper. In fact, BMI can compensate for the disadvantages of wages or education [ 65 ].

Secondly, we only have the message sending data and lack the reply data, which makes it impossible for us to study the interaction between users. Ranking effects caused by recommendation algorithms in online environments have been shown to influence the music people select [ 66 ] and the politicians people favor [ 67 ].

In real life, sending a message to another user is usually not affected by a single attribute. Fifthly, there are significant differences between Chinese and western cultures, and the website is only for heterosexual users, thus the conclusions of this paper may not be applicable to western society or homosexual people [ 68 , 69 ]. There are several avenues for future research.

We can examine the influence of recommendation algorithms on potential mate choice in online dating. We can also use the results obtained in the paper to further study the problem of stable matching for potential mate choice. The compatibility score between a female preference and the profile of the corresponding other side.

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Evol Hum Behav — Yancey G, Emerson MO Does height matter? An examination of height preferences in romantic coupling. J Fam Issues — Ward J What are you doing on Tinder? Impression management on a matchmaking mobile app. Inf Commun Soc — Ellison N, Heino R, Gibbs J Managing impressions online: self-presentation processes in the online dating environment. Any ideas, suggestions? For some reason I do not want to date a BBW lover been there done that and they made sure I stayed morbidly obese and said they loved me that way.

Just put a "few extra pounds", no harm their. Let them be the judge. Its online dating, you have nothing to lose. I would put "a few extra pounds" and let guys judge by your photos.

I think photos are much more telling than a little generic box. If your losing the weight, why not just consider yourself average? Go ahead and give them a shot, if they ask for more information you can tell them honestly, but I think you should let them be the judge of your body… They may have put average to avoid heavily obsess girls. Congrats on your success with the weight loss!! And good luck with online dating. Hey, you aren't the only one who doesn't especially like answering the body type question - they are pretty restrictive.

Am I "average" "thin" "a few extra pounds"? What defines a "few extra pounds" anyways? This a pet peeve of mine.

No, curvy doesn't mean "extra weight" it means a girl with big boobs, or possibly big hips and big butt. Think celebrities like Marilyn Monroe or J Lo, NOT celebs like Roseanne or Rosie O'Donnell. To the op, I would explain this to the guys. I know I'm not the best person to say this, because I have rejected guys for being obese especially those that claim they are "average".

If the guys had said to me that they were losing weight I would have considered them. Another option is to possibly wait until you lose the weight so this isn't an issue.

I think a picture speaks loader than just some dumb little box. Be sure to upload pictures so guys can see them and judge for themselves. Hm well I dunno.

I say I'm average. and about 25 pounds of muscle away from being overweight. So I guess I put average cause I'm in shape go to the gym all the time and am a big dude but I didn't put fit cause I should lose some weight.

Either way I don't really feel the need to explain in my profile I got pictures that are recent. If someone asked me on a date or in an email I'd explain how I stay in shape and what I do but if they don't I wont bother with it. There's a look for everyone, but I just hate the lying when people call themselves a bodytype they aren't. I am more of the muscular bodytype but wouldn't say I was "thin" or "petite". I'm not so I don't lie. Some guys aren't attracted to my bodytype, so are, but why make people waste their time on someone they wouldn't like?

Since you have about 50 more to go, you just need to put "few extra pounds". When they contact you, then that is the time to express your desire to go hiking and do those other things. I would not contact someone whose body type preferences are other than what your body type is.

I hate explaining my body type on internet. I'm curvy big chest and hips, small waist but I'm not fat nor overweight by north american definition or the BMI.

But apparently if you say you are curvy or average, everybody thinks that it means you are fat. I would just leave that option blank and let the pictures speak for themselves. We all have our preferences me. a slender to average-sized brunette with long hair, brown eyes, and glasses , but if a cute blonde with a few extra pounds flirted with me, I wouldn't turn her down. You never know. Yes I think that's true. People read these things like house details. You sound as though you are doing really well with your fitness - but 50 is more than 'a few extra'.

I'd leave it blank and post pics the trouble is that whatever you put it will mean somebody's filter blocks you. I've had several men filter me out because I don't have English in 'other languages' - that's because it's my mother tongue, derrrr!!!

I've never forgotten meeting someone who knew perfectly well that I am 5'2" tall and watching his face fall as he checked out my small but shapely!! It was so rude!! He could at least have made sure he didn't betray his disappointment quite so obviously I'm a leg man myself, and can say that nice legs don't have to be long legs at all.

His loss. Oddly, we're pretty close geographically too apparently, which is even more "bizarro". There's no such thing as anyone being out of anyone's league. If that was the case I wouldn't be pining for the one I like. I keep getting "you're too good for him" because he's not cute and doesn't make a lot of money. It doesn't bother me at all though.

Wow, he was turned off because you were 5'2? I can see if you lied and said you were 5'9 or something but if you told him that's rude. I once had a guy who was turned off because I told him my height and he liked smaller girls. The thing is he got mad after I met him.

Makes no sense. To the OP - be honest about your body type because you want to find a guy who will accept you. Good for you for trying to lose the weight and there is absolutely no way you should be apologizing to a guy in an introductory message. A guy will either be attracted to you or not. If you really want to date these guys and feel that your weight is what is holding you back from them wanting to date you, then I suggest you should stop trying to date and focus on the weight loss.

is "a few"? In what universe?! I'm sorry, but 50 lbs. too many is overweight by almost any standards. So how am I supposed to describe myself now? I'm by no means slender nor fat. I'd like to say average, but I'm pretty sure people also think that average is overweight or fat. If you and people you know think you're average, go with that. If not, go with a few extra pounds.

A few extra pounds means a few, like up to, say, 20? The MOST important thing is to just be honest and post full-body pictures of one-self that show the true you. If you're looking to date someone, they are going to see that "side" of you pretty soon anyway. Why not weed out those who are not attracted? I think I am average, but the bad thing is average in north america still means overweight.

But yeah, I will put average, for some reason I don't like to put pics up in an online dating site and I'm aware I won't be able to get many messages without a pic.

If I have to take online dating seriously, I guess I will have to upload pics as well By rov , August By FrankieG , April By Anonymous, April 8. By Sisi77 , January By Jakeissorry Started Monday at PM. By GordonFreeman Started Monday at AM. By boltnrun Started Yesterday at PM. By sysnoot Started Sunday at PM. Yahoo posted a blog entry in News , Sunday at AM. Newsweek posted a blog entry in News , Sunday at AM.

We estimate mate preferences using a novel data set from an online dating service. The data set contains detailed information on user attributes and the decision to contact a potential mate after viewing his or her profile. This decision provides the basis for our preference estimation approach.

A potential problem arises if the site users strategically shade their true preferences. We provide a simple test and a bias correction method for strategic behavior. The main findings are i There is no evidence for strategic behavior. ii Men and women have a strong preference for similarity along many but not all attributes. iii In particular, the site users display strong same-race preferences.

Race preferences do not differ across users with different age, income, or education levels in the case of women, and differ only slightly in the case of men. iv There are gender differences in mate preferences; in particular, women have a stronger preference than men for income over physical attributes.

This is a preview of subscription content, access via your institution. Rent this article via DeepDyve. Lee and Banerjee et al. The data used by Lee allow her to follow the users of a matchmaking service through several stages of the dating process until an eventual marriage, and she adds a learning component to the choice model.

Neither the names nor any contact information of the users were provided to us in order to protect the privacy of the users. In Hitsch et al. Biddle and Hamermesh report a Cronbach alpha of 0. Adachi shows a strategic substitutability property in that more selective behavior higher reservation utilities by women men leads, in equilibrium, to less selective behavior lower reservation utilities by men women. Although especially Adachi pushes the realism of these models significantly forward by allowing agents to possess very general preferences.

We estimated the model in MATLAB using the KNITRO nonlinear optimization solver. Instead of concentrating out the fixed effects, we estimated all fixed effects directly along with the preference parameters.

Using an analytic gradient and Hessian, convergence always occurred in less than 10 steps and in less than s. The main cost associated with sending an e-mail is the cost of composing it. The fear of rejection should be mitigated by the anonymity provided by the dating site. To be precise: The probability that m receives a reply from w is determined by the utility function U W x w , x m , i. the preference of a woman with attributes x w for a man with attributes x m.

We resample over individuals rather than individual choice instances to preserve within-person dependence structure.

The opposite interpretation of time on market is possible if bad types reveal their unobserved quality during a date, are then rejected and hence stay longer in the market.

The estimates based on the predicted reply probabilities without an excluded from the first-contact decision variable are similar. The effect is slightly positive and statistically significant for men in the 30—39 and 40—49 age groups, and statistically insignificant otherwise.

Most recent speed dating papers do not report age preferences, due to the small amount of variation in age among the students that comprise many of the analyzed samples.

The exception is Kurzban and Weeden , who consider only preferences over the age level, but not the age relative to a potential partner. More precisely, we estimate preferences over BMI differences that are at least 2 in absolute value. Using data from speed dating events, Eastwick et al. Their study, however, does not report gender differences.

Adachi, H. A search model of two-sided matching under nontransferable utility. Journal of Economic Theory, , — Article Google Scholar. Banerjee, A. Marry for what? Caste and mate selection in modern India.

Manuscript MIT. Becker, G. A theory of marriage: Part I. Journal of Political Economy, 81 4 , — Biddle, J. Journal of Labor Economics, 16 1 , — Browning, M. The economics of the family. Burdett, K. Marriage and class. Quarterly Journal of Economics, 1 , — Buss, D.

Sex differences in human mate preferences: Evolutionary hypotheses tested in 37 cultures. Behavioral and Brain Sciences, 12 , 1— The evolution of desire: Strategies of human mating. New York: Basic Books. Google Scholar. Sexual strategies theory: An evolutionary perspective on human mating. Psychological Review, 2 , — Choo, E.

Who marries whom and why. Journal of Political Economy, 1 , — Eagly, A. The origins of sex differences in human behavior. American Psychologist, 54 6 , — Eastwick, P. Sex differences in mate preferences revisited: Do people know what they initially desire in a romantic partner? Journal of Personality and Social Psychology, 94 2 , — Is love colorblind? Political orientation and interracial romantic desire.

Personality and Social Psychology Bulletin, 35 9 , — Etcoff, N. Survival of the prettiest: The science of beauty. New York: Doubleday Books. Finkel, E. Baumeister Attraction and rejection. Finkel Eds. New York: Oxford University Press. Fisman, R. Gender differences in mate selection: Evidence from a speed dating experiment. Quarterly Journal of Economics, 2 , — Racial preferences in dating.

Review of Economic Studies, 75 , — Gillis, J. The male-taller norm in mate selection. Personality and Social Psychology Bulletin, 6 3 , — Goldberger, A. Abnormal selection bias.

Karlin, T. Goodman Eds. New York: Academic. Hamermesh, D. Beauty and the labor market. American Economic Review, 84 5 , — Heckman, J. Sample selection bias as specification error.

Online Dating Deal-Breakers, Dating App Preferences, Filters,Introduction

 · Not everyone on dating apps are ready to date, wanting to date or being honest. Lots of patience, self-awareness, effort, good photos, decent writing skills, life experience, AdTop 10 Online Dating Services - Try the Best Online Dating ServicesWhether its instant messaging, video chat, dating games, offline events, or online Service catalog: Dating Wizard, Personalising Your Result, Safe & Secure P If someone says their ideal body type is 'athletic and muscular'. Single Peeps! Join. Submit your health and fitness tips for the chance to be featured in our New Year’s plan! Online AdEveryone Knows Someone Who's Met Online. Join Here, Browse For Free. Everyone Know Someone Who's Met Online. Start Now and Browse for blogger.com has been visited by 10K+ users in the past monthTypes: Meet the Young-at-Heart, Find Local Singles 40+, Get Matched Today AdCreate an Online Dating Profile for Free! Only Pay When You Want More Features! Make a Free Dating Site Profile! Only Pay When You're Ready to Start Communicating!  · Hi. I hate this part about selecting my body part. I am not morbidly obese anymore (YAY!) thanks to lapband and am just overweight but still have at least 50 more pounds to go ... read more

com has the following male body types to choose from:. Insanity is doing the same thing expecting different results. 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. I hate explaining my body type on internet. Behav Brain Sci —14 Article Google Scholar Trivers R Parental investment and sexual selection.

Trivers R Parental investment and sexual selection. Online dating body preferences betrayedgirlApril 29, in Dating Advice. View author publications. or rely on them exclusively to meet others instead of just another supplemental channel. is a vector characterizing whether female attributes meet male preferences for a pair of users 1 for yes and 0 for noand similarly male attr. Choosing the right appphotos, bios, messages go a long way but health, looks, work, mental health, exercise, social life, online dating body preferences, hobbies, and communication skills are oftentimes overlooked.

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