Data Mining Twitter to Improve Automated Vehicle Safety

Automated vehicle technologies may significantly improve driving safety, but only if they are widely adopted and if drivers use them appropriately. Prior work suggests that intentions to adopt new technology and appropriately rely on it are often driven by the user’s expectations. In recent years, these expectations increasingly depend on news presented on social media. For example, recent polls suggest that the majority of Twitter users primarily use the site as a news source. The power of social media in creating and changing expectations suggests that it may be a disruptive tool for increasing the adoption and safe use of automated vehicle technology. In this project, the research team seeks to understand the conversation about automated vehicles on Twitter through a network and natural language processing analysis. The team further focuses on responses and changes of opinion surrounding automated vehicle crashes. These analyses will identify a set of terms, key opinion generators, and hash tags that lead to the most accurate and positive responses to automated vehicles. In the final phase of the project, the team will translate these findings into guidelines for automated vehicle crash responses to help public information officers structure their communications about crashes. Research has shown that avoiding misinformation and structuring communication leads to improved outcomes in emergencies and thus the team expects these guidelines to facilitate automated vehicle safety.


  • English


  • Status: Completed
  • Funding: $283435
  • Contract Numbers:


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Safety through Disruption University Transportation Center (Safe-D)

    Virginia Tech Transportation Institute
    Blacksburg, VA  United States  24060
  • Project Managers:

    Glenn, Eric

  • Performing Organizations:

    Texas A&M Transportation Institute

    Texas A&M University System
    3135 TAMU
    College Station, TX  United States  77843-3135

    Virginia Tech Transportation Institute

    3500 Transportation Research Plaza
    Blacksburg, Virginia  United States  24061
  • Principal Investigators:

    McDonald, Tony

  • Start Date: 20190301
  • Expected Completion Date: 20201031
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01698761
  • Record Type: Research project
  • Source Agency: Safety through Disruption University Transportation Center (Safe-D)
  • Contract Numbers: 69A3551747115
  • Files: UTC, RIP
  • Created Date: Mar 12 2019 3:55PM