Data Mining Twitter to Improve Automated Vehicle Safety
| dc.contributor.author | McDonald, Anthony D. | en |
| dc.contributor.author | Huang, Bert | en |
| dc.contributor.author | Wei, Ran | en |
| dc.contributor.author | Alambeigi, Hananeh | en |
| dc.contributor.author | Arachie, Chidubem | en |
| dc.contributor.author | Smith, Alexander Charles | en |
| dc.contributor.author | Jefferson, Jacelyn | en |
| dc.date.accessioned | 2021-07-19T12:01:13Z | en |
| dc.date.available | 2021-07-19T12:01:13Z | en |
| dc.date.issued | 2021-02 | en |
| dc.description.abstract | Automated vehicle (AV) technologies may significantly improve driving safety, but only if they are widely adopted and used appropriately. Adoption and appropriate use are influenced by user expectations, which are increasingly being driven by social media. In the context of AVs, prior studies have observed that major news events such as crashes and technology announcements influence user responses to AVs; however, the exact impact and dynamics of this influence are not well understood. The goals of this project were to develop a novel search method to identify AV-relevant user comments on Twitter, mine these tweets to understand the influence of crashes and news events on user sentiment about AVs, and finally translate these findings into a set of guidelines for reporting about AV crashes. In service of these goals, we developed a novel semi-supervised constrained-level learning machine search approach to identify relevant tweets and demonstrated that it outperformed alternative methods. We used the relevant tweets identified to develop a topic model of AV events which illustrated that crashes, fault and safety, and technology companies were the most discussed topics following major events. While the sentiment among these topics was mostly neutral, tweets about crashes and fault and safety were negatively biased. We combined these findings with a series of interviews with Public Information Officers to develop a set of five basic guidelines for AV communication. These guidelines should aid proper public calibration and subsequent acceptance and use of AVs. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | http://hdl.handle.net/10919/104208 | en |
| dc.language.iso | en | en |
| dc.publisher | SAFE-D: Safety Through Disruption National University Transportation Center | en |
| dc.relation.ispartofseries | SAFE-D;04-098 | en |
| dc.rights | CC0 1.0 Universal | en |
| dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | en |
| dc.subject | automated vehicles | en |
| dc.subject | communication | en |
| dc.subject | marketing | en |
| dc.subject | social media | en |
| dc.subject | en | |
| dc.title | Data Mining Twitter to Improve Automated Vehicle Safety | en |
| dc.type | Report | en |
| dc.type.dcmitype | Text | en |