An exploration of the Facebook social networks of smokers and non-smokers.
An exploration of the Facebook social networks of smokers and non-smokers.
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DOI：
10.1371/journal.pone.0187332
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Graham AL
中科院分区：
文献类型：
--
作者：
Fu L;Jacobs MA;Brookover J;Valente TW;Cobb NK;Graham AL
社会网络影响健康行为，包括吸烟和戒烟。迄今为止，人们对在线吸烟者和非吸烟者的网络是否存在差异以及如何存在差异，以及这种差异对干预措施的潜在影响知之甚少。了解社会网络如何因吸烟状况而变化，可以为公共卫生工作提供信息，以加速戒烟或减缓烟草使用。这些二次分析探讨了吸烟者和非吸烟者的自我网络结构，这些数据是在Facebook上进行的随机对照试验的一部分。在试验期间，共有14010人安装了Facebook戒烟应用程序：9042名随机吸烟者，另外2881名不符合全部资格标准的吸烟者，以及2087名不吸烟者。所有个体的自我网络都被构建为二级连接。我们构建了四种网络：友谊网络、家庭网络、照片网络和团体网络。从这些网络中，我们测量了边缘、隔离、密度、平均中间度、传递性和平均接近度。我们还测量了直径、聚类和模块化，没有自我和孤立。Logistic回归以吸烟状况为响应，网络指标为主要自变量，人口统计和Facebook使用指标为协变量。这四个网络具有不同的特征，表现为不同的多重共线性问题和逻辑回归输出。在友谊网络中，吸烟的几率在中间性较低（p = 0.00）、传递性较低（p = 0.00）和直径较大（p = 0.00）的网络中较高。在家庭网络中，吸烟的几率在顶点较多（p = 0.01）、传递性较差（p = 0.04）和隔离较少（p = 0.01）的网络中较高。在照片网络中，没有一个网络指标能预测吸烟状况。在群体网络中，直径越小，吸烟的几率越高（p = 0.04）。综上所述，这些发现表明，与不吸烟者相比，该样本中的吸烟者的Facebook好友网络联系更少、更分散；更大但更破碎的家庭网络与更少的隔离；更紧密的集团网络；以及与非吸烟者网络结构相似的照片网络。这项研究说明了检查在线社交网络结构差异作为基于网络的干预措施的关键组成部分的重要性，并为未来研究基于个人健康行为的社交网络差异方式奠定了基础。在社会环境背景下，以个体行为为目标的干预措施将有助于理解参与者的社会网络结构。
Social networks influence health behavior, including tobacco use and cessation. To date, little is known about whether and how the networks of online smokers and non-smokers may differ, or the potential implications of such differences with regards to intervention efforts. Understanding how social networks vary by smoking status could inform public health efforts to accelerate cessation or slow the adoption of tobacco use. These secondary analyses explore the structure of ego networks of both smokers and non-smokers collected as part of a randomized control trial conducted within Facebook. During the trial, a total of 14,010 individuals installed a Facebook smoking cessation app: 9,042 smokers who were randomized in the trial, an additional 2,881 smokers who did not meet full eligibility criteria, and 2,087 non-smokers. The ego network for all individuals was constructed out to second-degree connections. Four kinds of networks were constructed: friendship, family, photo, and group networks. From these networks we measured edges, isolates, density, mean betweenness, transitivity, and mean closeness. We also measured diameter, clustering, and modularity without ego and isolates. Logistic regressions were performed with smoking status as the response and network metrics as the primary independent variables and demographics and Facebook utilization metrics as covariates. The four networks had different characteristics, indicated by different multicollinearity issues and by logistic regression output. Among Friendship networks, the odds of smoking were higher in networks with lower betweenness (p = 0.00), lower transitivity (p = 0.00), and larger diameter (p = 0.00). Among Family networks, the odds of smoking were higher in networks with more vertices (p = .01), less transitivity (p = .04), and fewer isolates (p = .01). Among Photo networks, none of the network metrics were predictive of smoking status. Among Group networks, the odds of smoking were higher when diameter was smaller (p = .04). Together, these findings suggested that compared to non-smokers, smokers in this sample had less connected, more dispersed Facebook Friendship networks; larger but more fractured Family networks with fewer isolates; more compact Group networks; and Photo networks that were similar in network structure to those of non-smokers. This study illustrates the importance of examining structural differences in online social networks as a critical component for network-based interventions and lays the foundation for future research that examines the ways that social networks differ based on individual health behavior. Interventions that seek to target the behavior of individuals in the context of their social environment would be well served to understand social network structures of participants.
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DOI：
10.1080/08964289.2015.1034645
发表时间：
2015
期刊：
Behavioral medicine (Washington, D.C.)
影响因子：
--
作者：
Latkin CA;Knowlton AR
通讯作者：
Knowlton AR
影响因子：
158.5
作者：
Christakis, Nicholas A.;Fowler, James H.
通讯作者：
Fowler, James H.
影响因子：
64.8
作者：
Bond, Robert M.;Fariss, Christopher J.;Jones, Jason J.;Kramer, Adamd. I.;Marlow, Cameron;Settle, Jaime E.;Fowler, James H.
通讯作者：
Fowler, James H.
影响因子：
2.9
作者：
Cobb NK;Jacobs MA;Saul J;Wileyto EP;Graham AL
通讯作者：
Graham AL
影响因子：
8
作者：
Mays, Darren;Gilman, Stephen E.;Niaura, Raymond S.
通讯作者：
Niaura, Raymond S.