Department of Biomedical Data Science, Dartmouth College, Lebanon, NH, United States.
Department of Epidemiology, Dartmouth College, Hanover, NH, United States.
J Med Internet Res. 2021 Sep 15;23(9):e27314. doi: 10.2196/27314.
Many social media studies have explored the ability of thematic structures, such as hashtags and subreddits, to identify information related to a wide variety of mental health disorders. However, studies and models trained on specific themed communities are often difficult to apply to different social media platforms and related outcomes. A deep learning framework using thematic structures from Reddit and Twitter can have distinct advantages for studying alcohol abuse, particularly among the youth in the United States.
This study proposes a new deep learning pipeline that uses thematic structures to identify alcohol-related content across different platforms. We apply our method on Twitter to determine the association of the prevalence of alcohol-related tweets with alcohol-related outcomes reported from the National Institute of Alcoholism and Alcohol Abuse, Centers for Disease Control Behavioral Risk Factor Surveillance System, county health rankings, and the National Industry Classification System.
The Bidirectional Encoder Representations From Transformers neural network learned to classify 1,302,524 Reddit posts as either alcohol-related or control subreddits. The trained model identified 24 alcohol-related hashtags from an unlabeled data set of 843,769 random tweets. Querying alcohol-related hashtags identified 25,558,846 alcohol-related tweets, including 790,544 location-specific (geotagged) tweets. We calculated the correlation between the prevalence of alcohol-related tweets and alcohol-related outcomes, controlling for confounding effects of age, sex, income, education, and self-reported race, as recorded by the 2013-2018 American Community Survey.
Significant associations were observed: between alcohol-hashtagged tweets and alcohol consumption (P=.01) and heavy drinking (P=.005) but not binge drinking (P=.37), self-reported at the metropolitan-micropolitan statistical area level; between alcohol-hashtagged tweets and self-reported excessive drinking behavior (P=.03) but not motor vehicle fatalities involving alcohol (P=.21); between alcohol-hashtagged tweets and the number of breweries (P<.001), wineries (P<.001), and beer, wine, and liquor stores (P<.001) but not drinking places (P=.23), per capita at the US county and county-equivalent level; and between alcohol-hashtagged tweets and all gallons of ethanol consumed (P<.001), as well as ethanol consumed from wine (P<.001) and liquor (P=.01) sources but not beer (P=.63), at the US state level.
Here, we present a novel natural language processing pipeline developed using Reddit's alcohol-related subreddits that identify highly specific alcohol-related Twitter hashtags. The prevalence of identified hashtags contains interpretable information about alcohol consumption at both coarse (eg, US state) and fine-grained (eg, metropolitan-micropolitan statistical area level and county) geographical designations. This approach can expand research and deep learning interventions on alcohol abuse and other behavioral health outcomes.
许多社交媒体研究都探讨了主题结构（如标签和子版块）识别与各种心理健康障碍相关信息的能力。然而，在特定主题社区中进行研究和建模的方法通常难以应用于不同的社交媒体平台和相关结果。使用 Reddit 和 Twitter 中的主题结构的深度学习框架可以为研究酗酒问题提供独特的优势，尤其是在美国的年轻人中。
本研究提出了一种新的深度学习管道，该管道使用主题结构来识别不同平台上的与酒精相关的内容。我们将我们的方法应用于 Twitter 上，以确定与酒精相关的推文的流行程度与从国家酒精滥用和酒精中毒研究所、疾病控制和预防中心行为风险因素监测系统、县健康排名和国家行业分类系统报告的酒精相关结果之间的关联。
双向转换器表示学习的神经网络学会将 1302524 个 Reddit 帖子分类为与酒精相关或非酒精相关的子版块。经过训练的模型从 843769 个随机推文的未标记数据集中识别出 24 个与酒精相关的标签。查询与酒精相关的标签可识别出 25558846 条与酒精相关的推文，包括 790544 条位置特定（地理标记）的推文。我们计算了与酒精相关的推文的流行程度与酒精相关结果之间的相关性，控制了年龄、性别、收入、教育和自我报告的种族等混杂因素的影响，这些因素由 2013-2018 年美国社区调查记录。
观察到以下显著关联：与酒精标签化推文和饮酒（P=.01）和酗酒（P=.005）相关，但与狂欢饮酒（P=.37）不相关，这是在都市区-大都市统计区层面上报告的；与酒精标签化推文和自我报告的过度饮酒行为（P=.03）相关，但与涉及酒精的机动车死亡事件（P=.21）不相关；与酒精标签化推文和啤酒厂（P<.001）、葡萄酒厂（P<.001）和啤酒、葡萄酒和白酒商店（P<.001）相关，但与饮酒场所（P=.23）不相关，以美国县和县级水平为单位；与酒精标签化推文和所有消耗的乙醇量（P<.001）以及消耗的乙醇来自葡萄酒（P<.001）和烈酒（P=.01）的来源，但不是啤酒（P=.63）相关，以美国州为单位。
在这里，我们提出了一种使用 Reddit 的酒精相关子版块开发的新颖自然语言处理管道，该管道可识别特定的酒精相关 Twitter 标签。所识别的标签的流行程度包含了关于在粗粒度（例如，美国州）和细粒度（例如，都市区-大都市统计区层面和县级）地理指定位置的酒精消费的可解释信息。这种方法可以扩展对酗酒和其他行为健康结果的研究和深度学习干预。
