Bike Frames: Understanding the Implicit Portrayal of Cyclists in the News

Increasing cycling for transportation or recreation can boost public health and reduce the environmental impacts of vehicles. However, news agencies' ideologies and reporting styles often influence public perception of cycling. For example, if news agencies overly report cycling accidents, it may make people perceive cyclists as "dangerous," reducing the number of opting to cycle. Additionally, a decline in cycling can result in less government funding for safe infrastructure. In this paper, we develop a novel prompting method to detect the perceived perception of cyclists within news headlines. To support this, we introduce a new dataset called "Bike Frames," which contains 31,480 news headlines and 1,500 human annotations. Our analysis focuses on 11,385 headlines from the United States. We also propose the BikeFrame Chain-of-Code (CoC) framework, which predicts cyclist perception, identifies accident-related headlines, and determines fault. This framework uses structured pseudocode to represent logical reasoning steps and incorporates news agency bias to enhance prediction accuracy, outperforming traditional chain-of-thought methods used in large language models. Most importantly, we find that incorporating news bias information significantly impacts performance, improving the average F1 score from .739 to .815. Finally, we conduct a comprehensive case study on U.S. news headlines, revealing differences in reporting between mainstream news agencies and cycling-specific websites, as well as variations in coverage based on the gender of cyclists. WARNING: This paper contains descriptions of accidents and death.