Positive Psychology Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Department of Computer Science, Stony Brook University, Stony Brook, New York, USA.
Alcohol Clin Exp Res. 2022 May;46(5):836-847. doi: 10.1111/acer.14807. Epub 2022 May 16.
Assessing risk for excessive alcohol use is important for applications ranging from recruitment into research studies to targeted public health messaging. Social media language provides an ecologically embedded source of information for assessing individuals who may be at risk for harmful drinking.
Using data collected on 3664 respondents from the general population, we examine how accurately language used on social media classifies individuals as at-risk for alcohol problems based on Alcohol Use Disorder Identification Test-Consumption score benchmarks.
We find that social media language is moderately accurate (area under the curve = 0.75) at identifying individuals at risk for alcohol problems (i.e., hazardous drinking/alcohol use disorders) when used with models based on contextual word embeddings. High-risk alcohol use was predicted by individuals' usage of words related to alcohol, partying, informal expressions, swearing, and anger. Low-risk alcohol use was predicted by individuals' usage of social, affiliative, and faith-based words.
The use of social media data to study drinking behavior in the general public is promising and could eventually support primary and secondary prevention efforts among Americans whose at-risk drinking may have otherwise gone "under the radar."
评估过度饮酒的风险对于从研究招募到有针对性的公共卫生信息传递等各种应用都很重要。社交媒体语言为评估可能有酗酒风险的个体提供了一个生态嵌入的信息来源。
我们使用从普通人群中收集的 3664 名受访者的数据，研究了基于酒精使用障碍识别测试-消费得分基准，社交媒体语言在多大程度上能准确地将个体分类为有酒精问题风险。
我们发现，当使用基于上下文单词嵌入的模型时，社交媒体语言在识别有酒精问题风险（即危险饮酒/酒精使用障碍）的个体时具有中等准确性（曲线下面积为 0.75）。个体使用与酒精、聚会、非正式表达、咒骂和愤怒相关的词语预示着高风险的酒精使用，而个体使用与社交、亲和和信仰相关的词语则预示着低风险的酒精使用。
使用社交媒体数据来研究普通人群的饮酒行为是有前途的，最终可能会支持那些有风险的饮酒行为可能“未被发现”的美国人的一级和二级预防工作。
