Research Papers

YouTube high risk driving videos: A content analysis of popular street racing videos

Version 1
Date added June 26, 2017
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Category 2017 CARSP XXVII Toronto
Tags Research and Evaluation, Session 3A
Author/Auteur Jane Seeley
Stream/Volet Research and Evaluation

Slidedeck Presentation Only (no paper submitted)

3A_4_Seeley

Abstract

Every month about 1,000,000,000 individuals view YouTube videos and young men are the heaviest users, overall streaming more YouTube videos and watching them longer than women and other age groups. This group is also the most dangerous group in traffic, engaging in more per capita violations and experiencing more per capita injuries and fatalities. YouTube contains many channels portraying risky driving. Yet, none has examined just what videos are most popular and their content. To document and analyze the content of a sample of YouTube high risk driving videos, including high risk driving activities, consequences, comments, likes/dislikes and to assess content in relation to views, sharing and feedback by YouTube viewers. A sample of YouTube videos with the highest view count were identified using the search term "street racing" on a given day. Identifying characteristics such as their URLs, user names, upload date, and number of subscribers, likes and dislikes and comments were recorded. Using a sample of the videos, a coding matrix was developed and tested for inter-rater reliability. A quantitative content analysis was conducted in which a template analysis style was used to code and analyze the data. Our strategy of searching for videos using the term "street racing" and filtering by view count yielding 186,000 videos. A sample of 30 most view videos were selected. Five videos were excluded because they did not meet the selection criteria. Almost all the videos were in the Cars & Vehicles category, but the Entertainment videos had higher counts. View counts ranged from 1,650,405 up to 9,045,488. 11 of the videos were produced by Users whose Channels contained more than 200 videos with at least 35,000 subscribers. Video characteristics coded include whether the video is a compilation or just one scenario; descriptors of the drivers, bystanders, and focus vehicles; the terrain and road conditions; type of video; driving activities; consequences/outcomes; video effects; comments and emotions. Independent coding has been completed. Analysis is ongoing. The presentation will summarize the results of the analysis in terms of the higher order themes identified in the videos for driving activities, consequences and outcomes. Conclusions will be drawn once analysis is completed.

Jane Seeley