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The structure of the sexual networks and partnership characteristics of young black men who have sex with men (MSM) may be contributing to their high risk of contracting HIV in the United States. Assortative mixing, which refers to the tendency of individuals to have partners from one’s own group, has been proposed as a potential explanation for disparities.
The objective of this study was to identify the age- and race-related search patterns of users of a diverse geosocial networking mobile app in seven metropolitan areas in the United States to understand the disparities in sexually transmitted infection and HIV risk in MSM communities.
Data were collected on user behavior between November 2015 and May 2016. Data pertaining to behavior on the app were collected for men who had searched for partners with at least one search parameter narrowed from defaults or used the app to send at least one private chat message and used the app at least once during the study period. Newman assortativity coefficient (R) was calculated from the study data to understand assortativity patterns of men by race. Pearson correlation coefficient was used to assess assortativity patterns by age. Heat maps were used to visualize the relationship between searcher’s and candidate’s characteristics by age band, race, or age band and race.
From November 2015 through May 2016, there were 2,989,737 searches in all seven metropolitan areas among 122,417 searchers. Assortativity by age was important for looking at the profiles of candidates with correlation coefficients ranging from 0.284 (Birmingham) to 0.523 (San Francisco). Men tended to look at the profiles of candidates that matched their race in a highly assortative manner with R ranging from 0.310 (Birmingham) to 0.566 (Los Angeles). For the initiation of chats, race appeared to be slightly assortative for some groups with R ranging from 0.023 (Birmingham) to 0.305 (Los Angeles). Asian searchers were most assortative in initiating chats with Asian candidates in Boston, Los Angeles, New York, and San Francisco. In Birmingham and Tampa, searchers from all races tended to initiate chats with black candidates.
Our results indicate that the age preferences of MSM are relatively consistent across cities, that is, younger MSM are more likely to be chatted with and have their profiles viewed compared with older MSM, but the patterns of racial mixing are more variable. Although some generalizations can be made regarding Web-based behaviors across all cities, city-specific usage patterns and trends should be analyzed to create targeted and localized interventions that may make the most difference in the lives of MSM in these areas.
Sexually transmitted infection (STI) and HIV transmission risk remain high among men who have sex with men (MSM) in the United States. MSM account for 68.2% of all primary and secondary syphilis cases in the United States in 2017 [
Young black MSM are at highest risk for HIV and STI transmission in the United States despite reporting similar frequency of HIV risk behaviors as other groups [
Assortative and disassortative mixing by age have also been found to be important to HIV transmission in particular contexts and are disparate by race [
Understanding men’s preferences using Web-based apps may lead to better hypotheses and understanding of behaviors and risks associated with STI transmission in the real world. Increasingly, MSM use various forms of technology and the internet to locate sexual partners outside of physical local venues [
Previous studies to estimate assortativity by age and race have focused on collecting egocentric and self-reported data from MSM through systemically or conveniently selected samples. Although important, these estimates are potentially limited by social desirability and recall biases of respondents [
Our study was designed to objectively identify the age- and race-related search patterns of users of a diverse GSN mobile app in seven major metropolitan areas in the United States to better understand the disparities in STI and HIV risk in MSM communities.
Data were collected on user behavior while using a diverse MSM GSN mobile app on an Apple or Android device between November 2015 and May 2016. Data entered by the user upon initiation of an account (including age, race, height, weight, and partner preferences) were collected where available. When signing up for a profile, individuals had the option of self-identifying their race as one of the following categories: Asian, Black, Caucasian, Latino, Middle Eastern, Mixed, Pacific Islander, or Other. We coded individuals into the following racial categories: Asian, black, white (indicated Caucasian), Latino, and other (indicated Middle Eastern, Mixed, Pacific Islander, or Other). Every time a user logged into the app and searched for a partner, information was collected on the parameters of the conducted search, including the following: GPS location of the searcher; the list of potential candidates resulting from the search; whether or not the user looked at the details of a candidate’s profile, favorited a candidate in the search list, or initiated a chat with a candidate in the search list; and the provided details of the candidate (including age, race, height, and weight). The data collected were composed of de-identified user identities for the searcher and the candidates resulting from the search.
Behavior on the app was collected for men who had searched for partners with at least 1 search parameter narrowed from defaults or used the app to send at least 1 private chat message to another individual and used the app at least once during the study period (November 2015 through May 2016). Users were categorized into 7 major metropolitan areas if their GPS coordinates fell within the metropolitan census tracts for the cities of Birmingham (Alabama), Boston (Massachusetts), Los Angeles (California), New York City (New York), San Francisco (California), Tampa (Florida), and Washington DC.
We had 2 main behavioral observations of interest: acquiring profile details and initiating a chat. If an individual clicked on a candidate’s name and looked at the profile details of a candidate in their search list, we considered this to be a situation in which details were acquired. If an individual initiated a chat with a candidate on their search list by sending a message to a candidate from the search list, we considered this to be a situation in which a chat was initiated (independent of chat length or duration).
An Institutional Review Board (Harvard TH Chan School of Public Health) and an independent ethics committee (Western Institutional Review Board) approved the study protocol and amendments for this study.
For each city, a mixing matrix (
A total of 2 quantities were calculated from the study data to understand assortativity patterns of men using the GSN app by age and race. Newman assortativity coefficient, R, a parameter used to assess the extent to which a population exhibits assortative, neutral, or disassortative sexual mixing patterns, was calculated from mixing matrices using the following equation:
where R is the assortativity coefficient and
An R value of 1 represents perfect assortativity in which people mix only with others having the same characteristics. An R value of 0 represents random mixing and an R value of −1 represents perfect disassortative mixing. We calculated Newman assortativity coefficient for racial categories and age categorized into 5-year increments up to the age of 40 years. As this measure compares mixing within the same group with mixing between groups, it is sensitive to the choice of band size for continuous measures such as age, with smaller age bands yielding lower values of assortativity. We, therefore, also calculated Pearson correlation coefficient to understand assortativity by age taken continuously to avoid this sensitivity and decrease it to outliers. Separate assortativity coefficients were calculated for each city of interest and each classification of the interaction (looking at candidate’s profile details or initiating a chat). When making reference to a specific group, such as men of Asian race/ethnicity, we define assortativeness for that group as a higher proportion of Asian candidates among the candidates of Asian searchers (the diagonal entry on the heat map) than among the candidates of all searchers (the corresponding marginal entry on the heat map).
On the basis of previous study, assortativity coefficients between 0.15 and 0.25 are considered minimally assortative, between 0.26 and 0.34 are considered moderately assortative, and 0.35 or larger are considered assortative [
Heat maps were used to help visualize the relationship between characteristics of the searcher and the candidate: age band, race, or age band and race. All values for heat maps were normalized by numbers of searchers in a category, thus the total value of a column (searcher) adds up to 100%. Besides (and above) each heat map, a column representing the distribution of candidates (and searchers) in the age, race, and age and race categories is shown. Heat maps were created for each city of interest and each classification of the interaction (looking at candidate’s profile details or initiating a chat).
From November 2015 through May 2016 there were 2,989,737 searches in all seven major metropolitan areas among 122,417 searchers. The median number of searches per searcher in all cities was 3 to 4, but there were outliers in each city. In New York and Washington DC, for example, some searchers had over 15,000 searches in the study period. All searches resulted in 752,832 unique candidates. The age and race profiles of the searchers and candidates for each metropolitan area are outlined in
The racial composition of the searchers varied by city. The majority of searchers self-identified as black or other, respectively, in Birmingham (77.72%, 3188/4102 and 13.99%, 574/4102) and Washington DC (64.96%. 17,699/27,245 and 20.67%, 5632/27,245) (
Characteristics of men who have sex with men that searched, or were candidates themselves resulting from a search, for partners on a diverse social networking app focusing on such men in seven US metropolitan cities, using the app between November 2015 and May 2016.
Category | Birmingham | Boston | Los Angeles | New York | San Francisco | Tampa | Washington DC | ||
N=72,793 | N=90,837 | N=265,376 | N=1,413,803 | N=219,768 | N=79,597 | N=847,563 | |||
Get details | 14,363 | 12,842 | 40,583 | 183,085 | 30,488 | 18,901 | 132,581 | ||
Chat | 4978 | 3309 | 10,756 | 51,166 | 6759 | 6175 | 40,429 | ||
N=4102 | N=4400 | N=13,132 | N=41,991 | N=10,833 | N=5925 | N=27,245 | |||
White | 211 (5.14) | 923 (20.98) | 1147 (8.73) | 2978 (7.09) | 1573 (14.52) | 670 (11.31) | 1867 (6.85) | ||
Black | 3188 (77.72) | 1088 (24.73) | 4534 (34.53) | 18,113 (43.14) | 2022 (18.67) | 3109 (52.47) | 17,699 (64.96) | ||
Latino | 35 (0.85) | 473 (10.75) | 2003 (15.25) | 5751 (13.70) | 919 (8.48) | 533 (9.00) | 934 (3.43) | ||
Asian | 94 (2.29) | 991 (22.52) | 2433 (18.53) | 4659 (11.10) | 3902 (36.02) | 224 (3.78) | 1113 (4.09) | ||
Other | 574 (13.99) | 925 (21.02) | 3015 (22.96) | 10,490 (24.98) | 2417 (22.31) | 1389 (23.44) | 5632 (20.67) | ||
N=15,633 | N=23,843 | N=50,682 | N=125,551 | N=48,222 | N=18,049 | N=75,867 | |||
White | 555 (3.55) | 3986 (16.72) | 5974 (11.79) | 13,239 (10.54) | 7016 (14.55) | 2069 (11.46) | 6518 (8.59) | ||
Black | 11,522 (73.70) | 4517 (18.94) | 13,641 (26.91) | 46,512 (37.05) | 7612 (15.79) | 9455 (52.39) | 41,175 (54.27) | ||
Latino | 294 (1.88) | 2048 (8.59) | 6036 (11.91) | 11,379 (9.06) | 3467 (7.19) | 1680 (9.31) | 3671 (4.84) | ||
Asian | 226 (1.45) | 9648 (40.46) | 15,275 (30.14) | 29,621 (23.59) | 22,333 (46.31) | 673 (3.73) | 9168 (12.08) | ||
Other | 3036 (19.42) | 3644 (15.28) | 9756 (19.25) | 24,800 (19.75) | 7794 (16.16) | 4172 (23.11) | 15,335 (20.21) | ||
N=4114 | N=4408 | N=13,183 | N=42,305 | N=10,929 | N=5937 | N=27,546 | |||
18-20 | 646 (15.70) | 553 (12.55) | 1119 (8.49) | 4371 (10.33) | 1235 (11.30) | 815 (13.73) | 2641 (9.59) | ||
21-24 | 1152 (28.00) | 1205 (27.34) | 3344 (25.37) | 10,375 (24.52) | 2627 (24.04) | 1586 (26.71) | 6017 (21.84) | ||
25-29 | 1215 (29.53) | 1364 (30.94) | 4686 (35.55) | 14,700 (34.75) | 3291 (30.11) | 1906 (32.10) | 9219 (33.47) | ||
30-34 | 597 (14.51) | 607 (13.77) | 2077 (15.76) | 6617 (15.64) | 1812 (16.58) | 760 (12.80) | 4725 (17.15) | ||
35-39 | 274 (6.66) | 315 (7.15) | 977 (7.41) | 3359 (7.94) | 950 (8.69) | 402 (6.77) | 2469 (8.96) | ||
40 and older | 230 (5.59) | 364 (8.26) | 980 (7.43) | 2883 (6.81) | 1014 (9.38) | 468 (7.88) | 2475 (8.98) | ||
N=15,814 | N=24,114 | N=51,124 | N=126,942 | N=48,770 | N=18,151 | N=76,908 | |||
18-20 | 2094 (13.24) | 2861 (11.86) | 4574 (8.95) | 12197 (9.61) | 4278 (8.77) | 2264 (12.47) | 7467 (9.71)) | ||
21-24 | 4704 (29.75) | 6783 (28.13) | 12,593 (24.63) | 30,259 (23.84) | 11,421 (23.42) | 4940 (27.22) | 17,987 (23.39) | ||
25-29 | 5134 (32.46) | 7959 (33.01) | 17,749 (34.72) | 43,800 (34.50) | 16,202 (33.22) | 6047 (33.31) | 26,530 (34.50) | ||
30-34 | 2173 (13.74) | 3421 (14.19) | 8223 (16.08) | 20,725 (16.33) | 8483 (17.39) | 2463 (13.57) | 12,644 (16.44) | ||
35-39 | 915 (5.79) | 1658 (6.88) | 4247 (8.31) | 10,548 (8.31) | 4441 (9.11) | 1223 (6.38) | 6416 (8.34) | ||
40 and older | 794 (5.02) | 1432 (5.38) | 3738 (7.31) | 9413 (7.42) | 3945 (8.09) | 1214 (6.69) | 5864 (7.62) |
Assortativity by age, as measured by Pearson correlation coefficient, ranged from 0.117 (Tampa) to 0.210 (Los Angeles) across all search activities.
Assortativity by age was important for looking at the profile details of candidates, with correlation coefficients ranging from 0.284 (Birmingham) to 0.523 (San Francisco) (
Assortativity results for race (top) and age (bottom) for each of the seven metropolitan areas.
Assortativity by age (years) for details that are acquired by searchers on candidates in Birmingham, Los Angeles, Boston, and New York. Heat maps show the relationship between searcher’s age band and the candidate’s age band. All values are normalized by numbers of searchers in each age category (the total value of a column adds up to 100%). Besides each heat map, a column representing the distribution of candidates in the age band and above each heat map, a row representing the distribution of searchers is shown.
Assortativity by age (years) for details that are acquired by searchers on candidates in San Francisco, Washington DC, and Tampa. Heat maps show the relationship between searcher’s age band and the candidate’s age band. All values are normalized by numbers of searchers in each age category (the total value of a column adds up to 100%). Besides each heat map, a column representing the distribution of candidates in the age band and above each heat map, a row representing the distribution of searchers is shown.
When initiating chats with candidates, searchers were not highly selective by age, with correlation coefficients ranging from 0.085 (Boston) to 0.148 (Washington DC) (
Assortativity by age (years) for chats initiated by searchers with candidates in Birmingham, Los Angeles, Boston, and New York. Heat maps show the relationship between searcher’s age band and the candidate’s age band. All values are normalized by numbers of searchers in each age category (the total value of a column adds up to 100%). Besides each heat map, a column representing the distribution of candidates in the age band and above each heat map, a row representing the distribution of searchers is shown.
Assortativity by age (years) for chats initiated by searchers with candidates in San Francisco, Washington DC, and Tampa. Heat maps show the relationship between searcher’s age band and the candidate’s age band. All values are normalized by numbers of searchers in each age category (the total value of a column adds up to 100%). Besides each heat map, a column representing the distribution of candidates in the age band and above each heat map, a row representing the distribution of searchers is shown.
Across all searches in all of the metropolitan areas examined, there was evidence for moderate assortativity by race with Newman assortativity coefficient R>0 for all cities. Across all search activities, R was highest in Los Angeles (0.33) and Boston (0.335) and lowest in Birmingham (0.088) and Tampa (0.149) (
Men tended to look at the details of candidates that matched their race in a highly assortative manner with Newman R coefficients ranging from 0.310 (Birmingham) to 0.566 (Los Angeles) (
Assortativity by race for details that are acquired by searchers on candidates in Birmingham, Los Angeles, Boston, and New York. Heat maps show the relationship between searcher’s race group and the candidate’s race group. All values are normalized by numbers of searchers in each race category (the total value of a column adds up to 100%). Besides each heat map, a column representing the distribution of candidates in the race group and above each heat map, a row representing the distribution of searchers is shown.
Assortativity by race for details that are acquired by searchers on candidates in San Francisco, Washington DC, and Tampa. Heat maps show the relationship between searcher’s race group and the candidate’s race group. All values are normalized by numbers of searchers in each race category (the total value of a column adds up to 100%). Besides each heat map, a column representing the distribution of candidates in the race group and above each heat map, a row representing the distribution of searchers is shown.
For the initiation of chats, race appeared to be assortative for some groups with R ranging from 0.023 (Birmingham) to 0.305 (Los Angeles) (
Assortativity by race for chats that are initiated by searchers on candidates in Birmingham, Los Angeles, Boston, and New York. Heat maps show the relationship between searcher’s race group and the candidate’s race group. All values are normalized by numbers of searchers in each race category (the total value of a column adds up to 100%). Besides each heat map, a column representing the distribution of candidates in the race group and above each heat map, a row representing the distribution of searchers is shown.
Assortativity by race for chats that are initiated by searchers on candidates in San Francisco, Washington DC, and Tampa. Heat maps show the relationship between searcher’s race group and the candidate’s race group. All values are normalized by numbers of searchers in each race category (the total value of a column adds up to 100%). Besides each heat map, a column representing the distribution of candidates in the race group and above each heat map, a row representing the distribution of searchers is shown.
When examining heat plots of both age and race together, we see that across searchers of various age/race combinations, 25 to 29-year-old black men are the most highly chatted with candidates, with similar trends for black men aged 20 to 24 and 30 to 34 years (
Assortativity by age (years) and race for chats initiated in Washington DC. Heat maps show the relationship between searcher’s age and race group and the candidate’s age and race group. All values are normalized by numbers of searchers in each age and race category (the total value of a column adds up to 100).
Assortativity by age (years) and race for chats initiated in New York. Heat maps show the relationship between searcher’s age and race group and the candidate’s age and race group. All values are normalized by numbers of searchers in each age and race category (the total value of a column adds up to 100).
Our results indicate that the age preferences of MSM are relatively consistent across cities, that is, younger MSM profiles are more likely to be viewed and chatted with compared with older MSM, but the patterns of racial mixing are more variable. Specifically, we see that men tend to look at profiles and access details for other men on the platform from similar age, race, and age/race subgroups, but men initiate chats with men aged between 20 and 29 years most often, independent of searcher age. Assortativity patterns with regard to age were similar to other studies done with MSM across the country. A study by Tieu et al [
Although we cannot, with certainty, generalize the interaction patterns of men in Web-based settings to real-world settings, Web-based partnership selection behavior may inform our understanding of real-world patterns in STI spread. Our observation of behavior on a GSN app, without relying on the self-reporting of behavior, is a major strength of our analysis. Similar to previous studies, our results confirm that black men are the most assortative by race with regard to viewing profiles and chatting with candidates. Specifically, our results lend evidence to patterns that suggest that black MSM are highly assortative in their preferences, consistent with propagation of HIV and other STIs on their sexual networks [
Viewing someone’s profile is considered a latent interaction and has been found to be extremely common among users of Web-based social networks and is often not reciprocated [
Caution should be used when employing and interpreting assortativity coefficients as they may mask subgroup behaviors that are important to sexual behavior and STI spread. Specifically, assortativity coefficients are less useful in areas where 1 or 2 groups make up the majority of the population (as in Tampa and Birmingham, in our examples). Therefore, comparing assortativity coefficients across metropolitan areas may only be useful together with information on the distribution of race or age in these areas. In addition, we are only reporting on the behavior of users on a single mobile app. Different mobile apps aimed at connecting MSM have different cultures and composition of users. The observations made from this mobile app may not be generalizable to other groups and apps. An added limitation is that we were not able to examine the behavior of men who did not specify a race or an age in their user profile. These men may have had a reason not to display an age or name (perhaps because they phenotypically belonged to a particular race or age group). The mixing observed is, therefore, limited to those individuals who specified their age or racial group. Finally, assortativity and age preferences are reported here at the population level, potentially obscuring interindividual variability. For example, an individual with 3 to 4 searches (the median) may have different assortativity patterns than an individual with 15,000 searches. These differences in patterns were not explored in this analysis and will be explored in future analyses.
Despite these limitations, this analysis has a number of strengths that provide a unique contribution to the field. Specifically, the results of our analysis include a number of different geographic regions within the United States. Our data also include a large number of MSM of color, and specifically black men, which allows for an objective examination of their behavior on the app. The data we are using are not egocentric or sociometric network data but rather observations on Web-based behavior. Our analysis is, therefore, not likely to be affected by social desirability bias, distortion, self-reporting biases, or recall bias similar to many other analyses examining assortative partnering behaviors among MSM.
One-size-fits-all interventions aiming to target MSM of color may not work in all contexts or among all minority subgroups. Specific geographic and community-level interventions need to be tailored to complement the needs of each individual population. Although some generalizations can be made regarding Web-based behaviors across all cities, city-specific usage patterns and trends should be analyzed to create targeted and localized interventions that may make the most difference in the lives of MSM in these areas.
global positioning system
geosocial networking
men who have sex with men
sexually transmitted infection
This study was supported by Grant Number U54GM088558 from the National Institute of General Medical Sciences. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health.
All authors (NNA, YAR, DN, YHG, GRS, KM, and ML) were involved in the conception and the design of the analysis. NNA and ML were responsible for performing the analysis. All authors were responsible for the interpretation of the study results and the writing of the paper.
DN is a former employee of the company that owns the GSN app discussed in this paper.