Introduction

The role of men who have sex with men (MSM) in the sexually transmitted diseases (STD) epidemic is considerable. In China, the national prevalence of HIV among MSM from 2001 to 2018 was estimated to be 5.7% [1]. The prevalence of HIV among student MSM was 4.4% [2]. Additionally, a meta-analysis reported that the HIV prevalence among MSM in high schools and colleges across China was also 3.8% [3]. Moreover, the prevalence of HIV among MSM who had heterosexual sex was significantly higher, at 9% [4]. In the United States, MSM comprised 53% and 67% of new HIV cases in 2006 and 2014, respectively [5,6]. Globally, MSM are experiencing an escalating risk of HIV infection, with the number of cases rising [7,8]. Furthermore, the death rate among MSM is much higher (8%) [9]. The HIV epidemic among Chinese college students has worsened due to technological advancements [10,11].

The utilization of online platforms and social media may elevate the risk of STD by facilitating the search for sexual partners [12]. In Germany, MSM represented 45.6% of users on online dating and social networking platforms [13]. In the USA, Rosenbaum et al. demonstrated that mobile social apps can facilitate the spread of syphilis and gonorrhea [14]. In South China, Guo reported that 63.8% of MSM utilized social software to seek sexual partners, emphasizing the geosocial app's significance in MSM communities [15]. Song's study showed that 53.7% of MSM reported having used gay apps to find sexual partners [11]. Hong et al. reported an HIV prevalence of 7.9% among gay app users and demonstrated that HIV infection was significantly associated with app use [16]. A behavior associated with higher-risk sexual practices and potential HIV infections, attributed to the rapid growth of internet technology [17].

The Blued, a prominent MSM mobile geosocial network App in China [18], plays a significant role in promoting healthy relationships among gay individuals and in advancing HIV prevention strategies, including the implementation of “Internet + HIV” models [19]. Internet and mobile apps revolutionize MSM dating, introducing discreet platforms [20,21]. Notably, Blued is widely used in the medical research, driving the optimized development of medical research and public health strategies [22,23]. According to a survey results, 98.2% of MSM in China have used gay apps at some point in their lives, with Blued being the most popular (97.5% usage rate). This is far higher than the usage of other platforms like Finka (18.4%), making Blued about 5.3 times more prevalent than other single platforms [24]. However, the lack of comprehensive MSM data in China presents significant challenges to the development and implementation of effective STD prevention strategies. The utilization of big data derived from social media platforms may offer a solution to this challenge, as technological advancements transform the socialization and partner-seeking behaviors of MSM.

Hainan Province, located in the southernmost region of China, is a key tourism and population hub near Southeast Asia. Between 2011 and 2019, young students in Hainan Province accounted for 5.99% of the annual HIV/AIDS cases. Notably, 91.38% of HIV-positive male students in higher education institutions acquired HIV through homosexual contact [25]. This trend indicated a potential uncontrolled transmission of HIV among college MSM students in Hainan. Accurately estimating MSM population within colleges is essential for calculating STD incidence in this group. However, the scarcity of comprehensive studies on college MSM in Hainan Province limits understanding and addressing the STD prevalence. To tackle this, we assessed the risks by estimating MSM population, distribution, and behaviors through Blued App to improve disease control.

Methods

Exploratory analysis

Multiplier method is perhaps the most common because of its simplicity and ease of use [26,27]. The basic principle of this method is that the count of individuals from the target population observed at designated institutions within a defined time period is equal to the total size of the estimated population multiplied by the proportion of the population who attended the selected institutions. The multiplier formulas to estimate the size of MSM are following (1,2), where N is the estimated size of student MSM, r is the number of recorded individuals; m is the multiplier, p is the proportion of recorded individuals among observed population. CI represents the confidence interval of population size.

(1)(2)

In this study, r represents the number of MSM registered on Blued, and p is the proportion of student MSM who use the Blued App, as obtained from the questionnaire survey. Since the total number of MSM among college students is unknown, we consider that and N are approximately equal n in this study, and thus N is used as a substitute for n to calculate the CI. The average number of registered on these dates was used as the observed individuals, and the Multiplier method was employed to estimate MSM.

Results

The usage of Blued App in the Hainan MSM population

In this study, we conducted a questionnaire survey involving 181 MSM residing in Hainan regarding the usage of the Blued App. Of the 181 participants, 164 individuals had utilized the Blued App (90.61%), with college students comprising 15.47% (28/181). Among the 28 student MSM, 25 (89.28%) students reported engaging with the Blued App. Of those who have utilized the app, 76.00% (19/25) have been doing so for a duration exceeding two years. Additionally, our research reveals that 76.00% (19/25) of these student MSM primarily use Blued for seeking sexual partners, while 56.00% (14/25) utilize it for social interaction (Table 1).

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Table 1. A survey on the usage of social media in student MSM.

https://doi.org/10.1371/journal.pone.0358788.t001

The number and behavior dynamics of college Blued users in Hainan Province

The data was collected from May 9th to 18th, 2023, over a period of ten consecutive days. The total number of online annotations was 3362. And 234 were eliminated for duplicate annotations and abnormal data, leaving 3128 as valid data. During the 10 day survey period, 2596 users had used the Blued App, and the average number of college students online on the Blued App was 939, while the total number of registered users was 1,579. Among these registered users, 2.34% (37/1,579) were identified as secret users (Table 2). According to the survey among MSM students, 89.28% reported being registered Blued (Table 1). Thus, the estimated MSM size in colleges across Hainan Province was 3504 (95%CI: 3464–3544).

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Table 2. The number of MSM using the Blued App in colleges on Hainan Island, China (n).

https://doi.org/10.1371/journal.pone.0358788.t002

The influencing factors of online activity of the MSM population at Hainan colleges and universities

We found that the proportion of active users was 33.13% (860/2596), and the proportion has statistically significant associations (P < 0.05) between age, college level, city, and various colleges, indicating their pivotal roles in shaping the online activity patterns of MSM in Hainan colleges. N represents the proportion of users who engaged with the Blued software online for at least five days within the total caseload. Notably, Danzhou and Wenchang, relatively small urban areas with limited Blued registrants, exhibited a high proportion of active users (42.22% and 41.30%). The multivariate logistic regression analysis identified that users aged 18−19 years (aOR = 1.49, 95%CI: 0.20–1.84) and 20−21 years (aOR=1.52, 95%CI: 1.26–1.85) exhibited a higher likelihood of being active users than individuals aged 22−24 years. Additionally, the proportion of active users among MSM from undergraduate colleges was 1.90 times (95%CI: 1.38–2.61) greater than that among those from junior colleges. In terms of the colleges’ category, the normal colleges (aOR = 0.55, 95%CI: 0.36−0.84) and the sports colleges (aOR = 0.27, 95%CI: 0.10−0.72) had a higher proportion of active users than comprehensive universities. (Table 3)

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Table 3. The determinants on the Blued App usage days of the student MSM in Hainan colleges, China. (n = 2596).

https://doi.org/10.1371/journal.pone.0358788.t003

Geographical distribution of MSM population in colleges and universities in Hainan Province

The results showed that the MSM population in colleges and universities in Hainan Province was mainly concentrated in Haikou, accounting for 72.22% (2259/3128), while Sanya ranked second with 20.75% (649/3128). The distribution of other cities was relatively scattered, with no notable patterns of aggregation established (Fig 1). We also found that the highest online activity was in Wenchang at 1.21% (19/1600), followed by Sanya at 0.82% (208/25169), and Haikou at 0.74% (663/89827) (Fig 2).

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Fig 1. The geographical distributions of student MSM in Hainan Province.

The blue dots in Fig 1 represent the estimated geographic densities of MSM populations in colleges across different regions of Hainan. The denser he blue dots are, the higher the concentration of MSM populations in that region, while areas with fewer dots indicate lower concentrations. Map source: National Standard Map Service Website of the Ministry of Natural Resources of the People's Republic of China (http://bzdt.ch.mnr.gov.cn/), Approval No. GS (2024) 0650.

https://doi.org/10.1371/journal.pone.0358788.g001

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Fig 2. The distribution of active users in cities and counties of colleges across Hainan Province.

https://doi.org/10.1371/journal.pone.0358788.g002

Discussion

The MSM population exhibits a higher risk for STD than the general population, including HIV, syphilis, hepatitis B virus, and other infections. Student MSM has also become a high-risk population for STD in China [2]. Understanding the size and distribution of the MSM population is crucial for effectively preventing AIDS and achieving the UNAIDS 95-95-95 targets by 2030 [28]. The significance of this study lies in providing data to support more effective prevention and control of the spread of STD among MSM by accurately estimating the size and distribution of the MSM population among male college students in Hainan.

We utilized the multiplier method in conjunction with the Blued weekend usage rate, as determined by a questionnaire survey, to estimate the size of the college student MSM across Hainan Province. The decision to focus on weekend usage rates was made because the extended duration of the workweek does not accurately represent daily usage patterns. The sample values for all universities fall within their respective 95% confidence intervals. The influence of the multiplier adjustment coefficient (0.893) on the overall population is negligible. Finally, we estimated the overall size of college student MSM cross Hainan Province was 3504.

At present, similar studies in China are relatively scarce, and they only focus on a few cities with relatively high social and economic status. This study focuses on a descriptive analysis of Blued-registered MSM in Hainan colleges, including geographic distribution and user characteristics. In Hainan, the Haikou City and Sanya City were identified as hotspots. The primary factor contributing to Haikou and Sanya is the concentration of universities in Hainan Province within these two cities. A comprehensive analysis suggests that this phenomenon may be associated with the regional level of economic development. Furthermore, economically developed metropolitan exhibit more active MSM communities, attributable to their superior network infrastructure and high levels of Internet penetration. This pattern is consistent with the geographic distribution characteristics of the MSM population in mainland China, where economically developed regions tend to have a more dispersed MSM population [29]. Thus, the economic development level might be an important factor in the distribution of social networking MSM.

The current study revealed that the proportion of Blued App users among male college students ranged from 0.44% to 6.67%, which is lower than the 3.7% to 8.5% reported in a previous study [30]. Our results indicated that a significant proportion of MSM students in Hainan colleges actively utilize the Blued App, predominantly for social interaction and seeking sexual partners. These results aligned with previous studies conducted in China [11]. In modern society, digital communication technologies, encompassing social media platforms and the Internet, have emerged as the predominant channels for MSM to facilitate social interactions and seek partners. These platforms expedite the establishment of social connections and the identification of sexual partners, potentially increasing the risk of HIV transmission [31,32]. Furthermore, these tools offer significant potential for identifying high-risk populations and improving risk surveillance.

Prior studies have demonstrated that the strategic use of social media can substantially increase the detection rate of HIV infections among MSM in Chinese colleges, thereby reducing sexual risk behaviors [22]. Moreover, a comprehensive management framework delivered via the Internet, which integrates HIV prevention, treatment, and associated technologies, has proven effective in reducing HIV incidence among MSM populations [33–35]. Previous research has highlighted the importance of software such as Blued in conjunction with Internet data sources to provide and deliver health knowledge. For example, this model could be used to push information about the correct use of condoms to people with suspicious high-risk behavior labels [36]. Additionally, considering that MSM frequently encounters discrimination and marginalization within social networks—obstacles that impede access to HIV and syphilis testing and treatment—leveraging the influence of key individuals within these networks may serve as an effective strategy to mitigate stigma and prejudice [37].

This study employed the multiplicative method to preliminarily explore the size of the MSM among college students. First, a potential coverage bias exists in the estimation of the MSM population size. The multiplier method relies on the proportion of MSM who use Blued (89.28%), which was derived from a convenience sample of 28 college students who self-identified as MSM. This sample may overrepresent gay-identified students who are active on geosocial apps, while underrepresenting bisexual male students, MSM but do not identify as gay, and those who do not use any dedicated gay apps. Consequently, the estimated coverage rate is likely an overestimate, and the total MSM population size derived from the multiplier method is likely an underestimate of the true MSM population. Therefore, our results should be interpreted as the lower bound of the MSM population among Hainan college students, or more conservatively, as the Blued-observable MSM population rather than the total MSM population

Furthermore, the inherent mobility of MSM individuals during the data collection period may violate this assumption, representing a limitation. To address this, we implemented three key strategies: (1) the college student population is relatively stable, (2) the surveys exclusively were conducted on weekdays to minimize transient population effects, and (3) the data collection was completed within a condensed timeframe to reduce exposure to migration influences. Additionally, while Blued App is a widely used platform among MSM in Chinese colleges, not all MSM individuals use this platform, potentially leading to an underestimation of the MSM population size. Lastly, some MSM individuals maintain multiple Blued App accounts simultaneously, which could result in double counting and consequently overestimate the size of the MSM population.

Conclusions

Blued App is widely used among student MSM in Hainan Province, especially for seeking sexual partners. Although Haikou and Sanya are the main hotspots, smaller cities like Danzhou and Wenchang show disproportionately high active user proportions. These results highlight the need for targeted sexual health education and STD surveillance in specific colleges, including those in less populous cities with high online sexual networking activity.

Supporting information

Acknowledgments

We thank Blued Inc. for providing the social networking platform. We also thank Liyang Huang, Yiguang Ou, Yuanhui Chen, Lu Yang, Tsering Qiongda, and Hongru Chen for assisting in data extraction and collation. The views expressed in this study are those of the authors and do not represent any institution.