Social Dynamics of Substance Use in Online Social Networks for Smoking Cessation

描述（由申请人提供）：每年有数百万吸烟者通过互联网寻求戒烟帮助，数十万吸烟者通过在线社交网络寻求戒烟建议和支持。尽管第一个戒烟在线社交网络问世已有近15年，但人们对在线社交网络影响烟草使用的方式知之甚少。这项研究将调查在线社交网络如何影响戒烟。我们的多学科行为和计算社会科学家团队将对一个大型在线戒烟社交网络的6年纵向数据集进行深入检查，该网络有超过65万名成员。除了烟草依赖治疗的核心组成部分外，还有多种交流模式（如论坛、私人信息、留言板）、自我表现模式（如个人档案、博客）和联系模式（如朋友）。所有操作都有日期/时间戳。该数据集包含所有用户的交易数据，包括来自“发帖者”的主动事件（例如，撰写消息和博客），来自“潜伏者”的被动事件（例如，阅读论坛帖子和个人资料），以及公共和私人通信的去识别内容。在这个网络中，有一项正在进行的随机试验的5000名参与者，以及2011年一项队列研究的1033名参与者。大约60%的参与者在3个月时的吸烟状况是已知的。这一独特的尚未开发的纵向社会网络数据宝库使研究社会动态对戒烟的影响成为可能。使用现代和创新的社会计算方法，我们将研究整个网络中的社会网络动态，情感和社会支持（nh65万）。我们将从社会网络分析开始，构建整个网络动态拓扑的度量。接下来，我们将进行复杂的情绪分析，以构建关于在线社区“热门话题”的社会影响动态指标（例如，“突然停止”与药物治疗，电子烟作为戒烟装置）。我们将使用细粒度的文本分析来解析与社会支持相关的语言（例如，信息，评估）和标志吸烟状态的语言（例如，戒烟纪念日，吸烟单）。我们还将审查与饮酒和问题饮酒有关的交流。这一范围广泛的指标将用于实现以下具体目标：(1)在N=5,000样本中检验社会网络动态、情绪动态和社会支持作为3个月戒烟的预测因子，并验证N=1,033样本中的结果；(2)审查与烟草和酒精使用有关的社会网络进程；(3)探讨从文本分析中识别的吸烟状况与3个月戒烟结果之间的对应关系，以确定估计在线社交网络戒烟率的可行性。这项研究将显著推进在线社交网络戒烟研究的方法。我们的工作将磨练已经广泛应用于其他领域的计算方法，但尚未告知烟草依赖治疗。

DESCRIPTION (provided by applicant): Millions of smokers turn to the Internet each year for cessation assistance and hundreds of thousands seek advice and support in online social networks for smoking cessation. Though it has been nearly 15 years since the first online social network for smoking cessation was launched, little is known about the ways in which online social networks impact tobacco use. This study will examine how online social networks influence smoking cessation. Our multidisciplinary team of behavioral and computational social scientists will conduct an in-depth examination of a 6-year longitudinal dataset from a large online social network for smoking cessation with over 650,000 members. In addition to core components of tobacco dependence treatment, there are multiple modes for communication (e.g., forums, private messaging, wall posts), self-representation (e.g., personal profiles, blogs) and affiliation (e.g., friends). All actions are date/time stamped. The dataset contains transactional data on all users, including active events from "posters" (e.g., writing messages and blogs), passive events from "lurkers" (e.g., reading forum posts and profiles), and de-identified content of public and private communications. Embedded within this network are N=5,000 participants in an ongoing randomized trial and a distinct sample of N=1,033 participants in a 2011 cohort study. Smoking status at 3 months is known on about 60% of these participants. This unique trove of as-yet unexplored longitudinal social network data makes it possible to examine the impact of social dynamics on smoking cessation. Using modern and innovative social computing methods, we will examine social network dynamics, sentiment, and social support in the entire network (NH650,000). We will begin with social network analyses to construct metrics of the dynamic topology of the overall network. Next, we will conduct sophisticated sentiment analyses to construct metrics of social influence dynamics regarding "hot topics" in the online community (e.g., "cold turkey" vs. pharmacotherapy, e- cigarettes as a cessation device). We will use fine-grained text analytics to parse language related to social support (e.g., informational, appraisal) and language that signals smoking status (e.g., quit anniversaries, smoking slips). We will also examine communications related to alcohol use and problem drinking. This broad range of metrics will be used to achieve the following Specific Aims: (1) Examine social network dynamics, sentiment dynamics, and social support as predictors of 3-month smoking abstinence in the N=5,000 sample, and validate results in the N=1,033 sample; (2) Examine social network processes related to tobacco and alcohol use; and (3) Explore the correspondence between smoking status discerned from text analytics and 3-month abstinence outcomes to determine the feasibility of estimating quit rates in online social networks for cessation. This study will significantly advance methods for the study of online social networks for smoking cessation. Our work will hone computational methods that have been broadly applied in other fields but have yet to inform tobacco dependence treatment.