Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States.
Department of Applied Health Science, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States.
J Med Internet Res. 2023 Jul 28;25:e48405. doi: 10.2196/48405.
Social media is an important information source for a growing subset of the population and can likely be leveraged to provide insight into the evolving drug overdose epidemic. Twitter can provide valuable insight into trends, colloquial information available to potential users, and how networks and interactivity might influence what people are exposed to and how they engage in communication around drug use.
This exploratory study was designed to investigate the ways in which unsupervised machine learning analyses using natural language processing could identify coherent themes for tweets containing substance names.
This study involved harnessing data from Twitter, including large-scale collection of brand name (N=262,607) and street name (N=204,068) prescription drug-related tweets and use of unsupervised machine learning analyses (ie, natural language processing) of collected data with data visualization to identify pertinent tweet themes. Latent Dirichlet allocation (LDA) with coherence score calculations was performed to compare brand (eg, OxyContin) and street (eg, oxys) name tweets.
We found people discussed drug use differently depending on whether a brand name or street name was used. Brand name categories often contained political talking points (eg, border, crime, and political handling of ongoing drug mitigation strategies). In contrast, categories containing street names occasionally referenced drug misuse, though multiple social uses for a term (eg, Sonata) muddled topic clarity.
Content in the brand name corpus reflected discussion about the drug itself and less often reflected personal use. However, content in the street name corpus was notably more diverse and resisted simple LDA categorization. We speculate this may reflect effective use of slang terminology to clandestinely discuss drug-related activity. If so, straightforward analyses of digital drug-related communication may be more difficult than previously assumed. This work has the potential to be used for surveillance and detection of harmful drug use information. It also might be used for appropriate education and dissemination of information to persons engaged in drug use content on Twitter.
社交媒体是越来越多人群的重要信息来源，可利用其来深入了解不断演变的药物过量流行情况。Twitter 可以提供有关趋势的有价值的见解，提供潜在用户可获得的通俗信息，以及网络和交互性如何影响人们接触到的内容以及他们如何围绕药物使用进行交流。
本探索性研究旨在调查使用自然语言处理进行无监督机器学习分析的方法，以识别包含物质名称的推文的连贯主题。
本研究涉及利用来自 Twitter 的数据，包括对品牌名（N=262607）和街头名（N=204068）处方药物相关推文的大规模收集，并使用无监督机器学习分析（即自然语言处理）对收集的数据进行可视化，以识别相关推文主题。进行潜在狄利克雷分配（LDA）和连贯性得分计算，以比较品牌（例如奥施康定）和街头（例如 oxy）名称的推文。
我们发现，人们根据使用的是品牌名还是街头名而以不同的方式讨论药物使用情况。品牌名称类别通常包含政治话题（例如，边境、犯罪和对正在进行的药物缓解策略的政治处理）。相比之下，包含街头名称的类别偶尔会提到药物滥用，但术语的多种社会用途（例如，Sonata）使主题的清晰度变得混乱。
品牌名称语料库中的内容反映了对药物本身的讨论，而很少反映个人使用情况。但是，街头名称语料库中的内容明显更加多样化，难以进行简单的 LDA 分类。我们推测这可能反映了使用俚语术语来秘密讨论与药物相关的活动。如果是这样，那么对数字药物相关交流的直接分析可能比以前假设的要困难。这项工作有可能用于监测和检测有害药物使用信息。它也可以用于向在 Twitter 上从事药物使用内容的人进行适当的教育和信息传播。
