Mothers Matter: Using Regression Tree Algorithms to Predict Adolescents’ Sharing of Drunk References on Social Media

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-sectiona...

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Autores principales:	Sebastian Kurten, David Winant, Kathleen Beullens
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning regression tree CART extreme gradient boosting social media adolescents Medicine R
Acceso en línea:	https://doaj.org/article/208d9ae4ba944ac0a8cc97bd04f0600f
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spelling	oai:doaj.org-article:208d9ae4ba944ac0a8cc97bd04f0600f2021-11-11T16:28:28ZMothers Matter: Using Regression Tree Algorithms to Predict Adolescents’ Sharing of Drunk References on Social Media10.3390/ijerph1821113381660-46011661-7827https://doaj.org/article/208d9ae4ba944ac0a8cc97bd04f0600f2021-10-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/21/11338https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601Exposure 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.Sebastian KurtenDavid WinantKathleen BeullensMDPI AGarticlemachine learningregression treeCARTextreme gradient boostingsocial mediaadolescentsMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 11338, p 11338 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning regression tree CART extreme gradient boosting social media adolescents Medicine R
spellingShingle	machine learning regression tree CART extreme gradient boosting social media adolescents Medicine R Sebastian Kurten David Winant Kathleen Beullens Mothers Matter: Using Regression Tree Algorithms to Predict Adolescents’ Sharing of Drunk References on Social Media
description	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.
format	article
author	Sebastian Kurten David Winant Kathleen Beullens
author_facet	Sebastian Kurten David Winant Kathleen Beullens
author_sort	Sebastian Kurten
title	Mothers Matter: Using Regression Tree Algorithms to Predict Adolescents’ Sharing of Drunk References on Social Media
title_short	Mothers Matter: Using Regression Tree Algorithms to Predict Adolescents’ Sharing of Drunk References on Social Media
title_full	Mothers Matter: Using Regression Tree Algorithms to Predict Adolescents’ Sharing of Drunk References on Social Media
title_fullStr	Mothers Matter: Using Regression Tree Algorithms to Predict Adolescents’ Sharing of Drunk References on Social Media
title_full_unstemmed	Mothers Matter: Using Regression Tree Algorithms to Predict Adolescents’ Sharing of Drunk References on Social Media
title_sort	mothers matter: using regression tree algorithms to predict adolescents’ sharing of drunk references on social media
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/208d9ae4ba944ac0a8cc97bd04f0600f
work_keys_str_mv	AT sebastiankurten mothersmatterusingregressiontreealgorithmstopredictadolescentssharingofdrunkreferencesonsocialmedia AT davidwinant mothersmatterusingregressiontreealgorithmstopredictadolescentssharingofdrunkreferencesonsocialmedia AT kathleenbeullens mothersmatterusingregressiontreealgorithmstopredictadolescentssharingofdrunkreferencesonsocialmedia
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Mothers Matter: Using Regression Tree Algorithms to Predict Adolescents’ Sharing of Drunk References on Social Media

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