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，并在以有可能用于改善公共卫生的方式推进科学方面采取重要步骤。这些调整将允许对酒精或药物滥用(AOD)治疗中个人产生的大量社交媒体数据进行处理和有意义的解释，并使我们能够解决3个目标。1)它将允许使用该数据源来识别可能被用来识别药物使用复发和治疗中断的高危个人的社交媒体内容；2)它将描述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话题进行公开对话的频率和模式；以及3)它将帮助确定与AOD治疗患者接触的最佳社交媒体平台。研究人员将从4个以社区为基础的药物滥用治疗项目(总共11个地点)中招募1000名患者进入无药物门诊AOD治疗。参与者将完成电池摄入和社交媒体使用情况调查，每周报告他们的酒精和药物使用情况，允许从他们的临床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。为了解决第一个目标，社交媒体数据将使用差异语言分析(DLA)进行分析，这是一种开放的词汇技术，不依赖于关于复发和治疗中断的预先设想的理论，但允许数据本身驱动对语言的包容性探索。它找到单词、短语和主题，并使用词云来表示它们，但与大多数词云不同的是，DLA根据单词或短语与测试变量之间的关系的强度对单词进行缩放。这种开放词汇的方法具有很好的潜力，可以揭示新的见解，帮助我们理解与复发和治疗中断相关的风险因素、态度和行为。最终，这些信息可以用于在社交媒体应用程序的开发中生成算法，当个人面临复发和治疗中断的风险时，这些应用程序将为他们提供额外的支持，或者当患者完全参与治疗时，这些信息可以为他们的努力提供应有的认可。确定对治疗保留和持续恢复产生不利影响的因素是当务之急。在接受治疗的患者中，只有不到45%的人完成了治疗，据报道，12个月后复发率高达92%，大多数患者在3个月内复发。识别预测治疗、辍学或药物使用的社交互动，并在此之前自动发送信息进行干预，可能会改善和延长美国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.