Distracted Driving on YouTube: Categorical and Quantitative Analyses of Messages Portrayed (Preprint)
Notice bibliographique
Résumé
BACKGROUND Distracted driving is a global epidemic, injuring and killing thousands of people every year. To better understand why people still engage in this dangerous behavior, we need to assess how the public gets informed about this issue. Knowing that many people use the internet as their primary source of initial research on topics of interest, we conducted an assessment of popular distracted driving videos found on YouTube. OBJECTIVE This study aimed to gauge the popularity of distracted driving videos and to assess the messages portrayed by classifying the content, context, and quality of the information available on YouTube. METHODS We conducted a search on YouTube using 5 different phrases related to distracted driving. Videos with more than 3000 views that mentioned or portrayed any aspect of distracted driving were identified, collected, and analyzed. We measured popularity by the number of videos uploaded annually and the number of views and reactions. Two independent researchers reviewed all the videos for categorical variables. Content variables included distractions; consequences; orthopedic injuries; and whether the videos were real accounts, reenactments, fictitious, funny, serious, and graphic. Context variables assessed the setting of the events in the video, and quality of information was measured by the presence of peer-reviewed studies and inclusion and referencing of statistics. Discrepancies in data collection were resolved by consensus via the coding authors. A comparative subanalysis of the 10 most viewed videos and the overall results was also done. RESULTS The study included a total of 788 videos for review, uploaded to YouTube from 2006 to 2018. An average of 61 videos with greater than 3000 views were uploaded each year (SD 34.6, range 3-113). All videos accumulated 223 million views, 104 million (46.50%) of them being among the 10 most viewed videos. The top 3 distractions depicted included texting, talking on the phone, and eating and/or drinking. Motor vehicle crashes (MVCs) and death were depicted in 742 (94.2%) videos, whereas 166 (21.1%) of the videos depicted injuries. Orthopedic injuries were described in 90 (11.4%) videos. Furthermore, 220 (27.9%) of the videos contained statistics, but only 27 (3.7%) videos referenced a peer-reviewed study. CONCLUSIONS This study demonstrates that there is a high interest in viewing distracted driving videos, and the popularity of these videos appears to be relatively stable over time on a forum that fluxes based on the current opinions of its users. The videos mostly focused on phone-related distractions, overlooking many other equally or more common forms of distracted driving. Death, which in reality is a far less common distracted driving consequence than injuries, was portrayed 1.7 times as much. Surprisingly, orthopedic injuries, which lead to a massive source of long-term disability and often result from MVCs, are vastly underrepresented.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,052 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».