Social network dynamics and HIV transmission

Rothenberg, Richard; Potterat, John J.; Woodhouse, Donald E.; Muth, Stephen Q.; Darrow, William W.; Klovdahl, Alden S.

doi:10.1097/00002030-199812000-00016

Cited by 204 publications

(158 citation statements)

References 19 publications

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“…The role of perceived social or peer norms is perhaps the most researched component of this theoretical explanatory model, 122 and there is bountiful empirical evidence that both actual and perceived social norms of behavior influence sexual behaviors, including condom use, 123,124 sexual debut, 125,126 and other sexual risk behaviors among heterosexual populations. [127][128][129] In addition, there is a growing focus on the role of social networks, as opposed to sexual networks, and their influence on HIV and STI infection and transmission, [130][131][132][133][134][135][136][137][138][139][140][141][142] sexual behavior [143][144][145][146][147][148] and sexual HIV risk behavior.…”

Section: The Urban Environment and Sexual Behavior: Extant Explanatormentioning

confidence: 99%

The Urban Environment and Sexual Risk Behavior among Men who have Sex with Men

Frye

Latka²,

Koblin³

et al. 2006

J Urban Health

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Increasingly, studies show that characteristics of the urban environment influence a wide variety of health behaviors and disease outcomes, yet few studies have focused on the sexual risk behaviors of men who have sex with men (MSM). This focus is important as many gay men reside in or move to urban areas, and sexual risk behaviors and associated outcomes have increased among some urban MSM in recent years. As interventions aimed at changing individual-level risk behaviors have shown mainly short-term effects, consideration of broader environmental influences is needed. Previous efforts to assess the influence of environmental characteristics on sexual behaviors and related health outcomes among the general population have generally applied three theories as explanatory models: physical disorder, social disorganization and social norms theories. In these models, the intervening mechanisms specified to link environmental characteristics to individual-level outcomes include stress, collective efficacy, and social influence processes, respectively. Whether these models can be empirically supported in generating inferences about the sexual behavior of urban MSM is underdeveloped. Conceptualizing sexual risk among MSM to include social and physical environmental characteristics provides a basis for generating novel and holistic disease prevention and health promotion interventions.

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Section: The Urban Environment and Sexual Behavior: Extant Explanatormentioning

confidence: 99%

The Urban Environment and Sexual Risk Behavior among Men who have Sex with Men

Frye

Latka²,

Koblin³

et al. 2006

J Urban Health

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“…However, if the information is more likely to spread virally, developing a successful strategy gets more complicated because viral diffusion depends on the structure of the underlying social networks. For example, information in a cohesive network -where users are well-connected with each other -spreads relatively fast [9]. One strategy for health communication would then be to identify cohesive subcommunities within a network and then spread the information in each sub-community.…”

Section: Discussion Principal Resultsmentioning

confidence: 99%

How did Ebola Information Spread on Twitter: Broadcasting or Viral Spreading? (Preprint)

Liang¹,

Fung²,

Tse³

et al. 2018

Preprint

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Background: It has been argued that information and emotions towards public health issues could spread widely through online social networks. Although aggregate metrics on the volume of information diffusion are available, we know little about how information spreads on online social networks. For example, health information could be transmitted from one to many (i.e. broadcasting), which is similar to how traditional mass media passes information to the general public. Health information could also be transmitted from many to many (i.e. viral spreading), which is analogous to the spread of infectious diseases. Objective: The aim of this study is to determine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages. On Twitter, influential users are those whose tweets receive a large number of retweets. Methods: Our data was purchased from GNIP, the official Twitter data provider. We obtained all Ebola-related tweets (including retweets and replies) posted from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships (who follows whom on Twitter). Social network analysis was performed to investigate retweeting patterns. In addition to describing the diffusion structures, we classify users in the network into four categories (i.e., influential user, hidden influential user, disseminator, common user) based on following and retweeting patterns. Disseminators received fewer retweets than expected based on their number of followers, common users and influential users received as many or fewer retweets than expected, and hidden influential users received more retweets than expected. Results: On average, 91% of the retweets were directly retweeted from the initial message. Moreover, 47.5% of the retweeting paths of the original tweets had a depth of 1 (i.e., from the seed user to its immediate followers). These observations suggested that the broadcast model was more pervasive than viral spreading. Furthermore, we found that influential users and hidden influential users can trigger more retweets than disseminators and common users. Disseminators and common users relied more on the viral model for spreading information beyond their immediate followers via influential and hidden influential users. Conclusions: The broadcast model was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work with influential and hidden influential users to get the message across, because influential and hidden influential users can reach more people that are not following the public health Twitter accounts. Although both influential users and hidden influential users can trigger a lot of retweets, recognizing and using the hidden influential users as the source of information could potentially be a cost-effective communication strategy for public health promotion, because the hidden influential users can receive...

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“…Exemplo 4: A dinâmica das redes e a transmissão do HIV Rothenberg et al (1998) realizaram estudo prospectivo em uma rede de pessoas sob risco presumivelmente elevado para a infecção pelo HIV em comunidade com baixa prevalência e baixa transmissão endógena. Procuraram verificar como a dinâmica das mudanças nessa rede influenciavam a transmissão do HIV.…”

Section: Exemplo 2: Simulação De Rede Com Parcerias Concomitantesunclassified