Predicting AOD Relapse and Treatment Completion from Social Media Use

描述（由申请人提供）：公共卫生研究和实践尚未利用传播媒体的新变化，尽管分析社交媒体互动的方法和工具已经开发出来，并成功地用于营销和商业，以更好地瞄准潜在客户，定制产品和预测行为。这项研究将调整这些工具，并采取重要步骤，以有可能用于改善公共卫生的方式推进科学。这些调整将允许处理和有意义地解释酒精或药物滥用（AOD）治疗中个人产生的大量社交媒体数据，并使我们能够实现三个目标。1)它将允许使用该数据源来识别社交媒体内容，这些内容可能用于识别物质使用复发和治疗退出的高风险个体；2)它将提供关于AOD患者在社交媒体上就酒精和药物信息和使用以及治疗信息等主题进行公开对话的频率和模式的描述；3)它将有助于确定最佳的社交媒体平台，以接触到接受AOD治疗的个人。研究人员将从4个基于社区的药物滥用治疗项目（共11个站点）招募1000名进入无药物门诊AOD治疗的患者。参与者将完成一份关于他们的社交媒体使用情况的摄入电池和调查，每周报告他们的酒精和药物使用情况，允许从他们的诊所记录中提取治疗进入和出院数据，并从他们的Facebook和Twitter账户中提取数据。为了实现第一个目标，社交媒体数据将使用差异语言分析（DLA）进行分析，这是一种开放词汇技术，不依赖于关于复发和治疗退出原因的预先设想的理论，而是允许数据本身推动对语言的包容性探索。它查找单词、短语和主题，并使用单词云来呈现它们，但与大多数单词云不同的是，DLA根据单词或短语与被测试变量之间的关系强度来缩放单词。这种开放词汇的方法极有可能揭示新的见解，帮助我们理解与复发和治疗退出相关的风险因素、态度和行为。最终，这些信息可以用于生成社交媒体应用程序开发中的算法，当个人面临复发和治疗退出的风险时，这些算法将为他们提供额外的支持，或者当患者完全参与治疗时，为他们的努力提供应有的认可。确定影响治疗保持和持续恢复的不利因素是必要的。接受治疗的患者中只有不到45%完成了治疗，据报道12个月时复发率高达92%，其中大多数在3个月内复发。识别预测治疗退出或药物使用的社会互动，并在这种情况发生之前自动发送信息进行干预，可以改善和延长美国2220万药物依赖者的生命。

DESCRIPTION (provided by applicant): Public health research and practice have not yet taken advantage of emerging changes in communication media even though methods and tools to analyze social media interactions have been developed and successfully used in marketing and business to better target prospective customers, tailor products, and predict behavior. This research will adapt these tools and take important steps in advancing science in ways that have potential to be used to improve public health. These adaptations will allow processing and meaningful interpretation of large volumes of social media data generated by individuals in Alcohol or Drug Abuse (AOD) treatment and allow us to address 3 aims. 1) It will allow use of this data source for identifying social media content that might be used to identify individuals who are at high risk for substance use relapse and treatment dropout; 2) It will provide a description of the frequency and patterns of AOD patients' public dialogue on social media with respect to topics such as alcohol and drug information and use, as well as treatment information; and 3) It will help to identify the best social media platforms to reach individuals i AOD treatment. Research staff will recruit 1,000 patients entering drug-free outpatient AOD treatment from 4 community based substance abuse treatment programs (a total of 11 sites). Participants will complete an intake battery and survey of their social media use, report weekly on their alcohol and drug use, give permission to extract treatment entry and discharge data from their clinic records, and to extract data from their Facebook and Twitter accounts. To address the first aim, social media data will be analyzed using Differential Language Analysis (DLA), an open-vocabulary technique that does not rely on pre-conceived theories regarding reasons for relapse and treatment dropout, but allows the data itself to drive an inclusive exploration of language. It finds words, phrases, and topics and presents them using word clouds, but unlike most word clouds, which scale words by their frequency, DLA scales words according to the strength of the relationship between the word or phrase and the variable tested. This open-vocabulary approach has excellent potential to reveal new insights to aid our understanding of risk factors, attitudes, and behaviors associated with relapse and treatment dropout. Eventually this information could be used to generate algorithms in the development of social media applications that would provide additional support for individuals when they are at risk for relapse and treatment dropout, or provide deserved acknowledgement for efforts when patients are fully engaged in treatment. Identifying factors that adversely affect treatment retention and sustained recovery is imperative. Less than 45% of the patients who enter treatment complete it and relapse rates have been reported as high as 92% at 12 months, with most relapsing within 3 months. Identifying social interactions that predict treatment dropout or substance use and automatically sending messages to intervene before that happens could improve and extend the lives of the 22.2 million drug-dependent individuals in the US.