The Impact of Autonomous Vehicle Accidents on Public Sentiment: A Decadal Analysis of Twitter Discourse Using roBERTa - Romy Sauvayre; Jessica S. M. Gable; Adam Aalah; Melvin Fernandes Novo; Maxime Dehondt; Cédric Chauvière (Technologies)
In the field of autonomous vehicle (AV) acceptance and opinion studies, questionnaires are widely used. Additionally, AV experiments and driving simulations are utilized. However, few AV studies have investigated social media, and fewer studies have analyzed the impact of AV crashes on public opinion, often relying on limited social media datasets. This study aims to address this gap by exploring a comprehensive dataset of six million tweets posted over a decade (2012–2021), and neural networks, sentiment analysis and knowledge graphs are applied. The results reveal that tweets predominantly convey negative sentiment (40.86%) rather than positive (32.52%) or neutral (26.62%) sentiment. A binary segmentation algorithm was used to distinguish an initial positive sentiment period (January 2012–May 2016) followed by a negative period (June 2016–December 2021), which was initiated by a fatal Tesla accident and reinforced by a pedestrian killed by an Uber AV. The sentiment polarity exhibited in the posted tweets was statistically significant (U = 24,914,037,786; p value < 0.001). The timeline analysis revealed that the negative sentiment period was initiated by fatal accidents involving a Tesla AV driver and a pedestrian hit by an Uber AV, which was amplified by the mainstream media.