Objective: Conduct an exploratory analysis of driver distraction tweets using text mining. Background: Twitter is a popular social networking site with a wealth of data that is both explanatory and predictive of current trends and events. Data from Twitter may also prove useful in understanding the attitudes and opinions surrounding distracted driving. Method: Tweets posted between January 29, 2012 and April 12, 2013 containing the words ‘driver distraction’ or ‘driving distraction’ were collected. Text mining was used to extract patterns from the tweets in terms of timelines, frequencies, and associations. Results: Over 8,000 tweets were collected and contained information about users’ personal experience with driver distraction as well as various news articles about driver distraction. Conclusion: Twitter data provide a real-time snapshot of the attitudes surrounding of distracted driving. Application: Information from social media can complement traditional driving data sources, such as simulator studies, naturalistic studies, and epidemiological data, to create a more holistic picture of distracted driving.
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