Faculty of Social Sciences, Leuven School for Mass Communication Research, KU Leuven, Parkstraat 45, 3000 Leuven, Belgium.
Department of Electrical Engineering, Dynamical Systems, Signal Processing and Data Analytics (STADIUS), KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium.
Int J Environ Res Public Health. 2021 Oct 28;18(21):11338. doi: 10.3390/ijerph182111338.
Exposure to online drinking on social media is associated with real-life alcohol consumption. Building on the Theory of planned behavior, the current study substantially adds to this line of research by identifying the predictors of sharing drunk references on social media. Based on a cross-sectional survey among 1639 adolescents with a mean age of 15 (59% female), this study compares and discusses multiple regression tree algorithms predicting the sharing of drunk references. More specifically, this paper compares the accuracy of classification and regression tree, bagging, random forest and extreme gradient boosting algorithms. The analysis indicates that four concepts are central to predicting adolescents' sharing of drunk references: (1) exposure to them on social media; (2) the perceived injunctive norms of the mother towards alcohol consumption; (3) the perceived descriptive norms of best friends towards alcohol consumption; and (4) willingness to drink alcohol. The most accurate results were obtained using extreme gradient boosting. This study provides theoretical, practical, and methodological conclusions. It shows that maternal norms toward alcohol consumption are a central predictor for sharing drunk references. Therefore, future media literacy interventions should take an ecological perspective. In addition, this analysis indicates that regression trees are an advantageous method in youth research, combining accurate predictions with straightforward interpretations.
社交媒体上的网络饮酒暴露与现实生活中的饮酒行为有关。本研究以计划行为理论为基础，通过确定在社交媒体上分享醉酒参考资料的预测因素，为这一研究领域做出了重要贡献。本研究基于对 1639 名年龄在 15 岁左右（女性占 59%）的青少年进行的横断面调查，比较和讨论了多种回归树算法对社交媒体上分享醉酒参考资料的预测。具体来说，本文比较了分类和回归树、套袋法、随机森林和极端梯度增强算法的准确性。分析表明，有四个概念是预测青少年分享醉酒参考资料的核心：（1）在社交媒体上接触到它们；（2）母亲对饮酒的感知规范；（3）最好的朋友对饮酒的感知描述规范；以及（4）饮酒意愿。使用极端梯度增强算法获得了最准确的结果。本研究提供了理论、实践和方法学方面的结论。它表明，母亲对饮酒的规范是分享醉酒参考资料的核心预测因素。因此，未来的媒体素养干预措施应采取生态视角。此外，本分析表明，回归树在青少年研究中是一种有利的方法，它结合了准确的预测和简单的解释。
