  Increasing cycling for transportation or recreation can boost 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 cyclists who
opt to cycle. Additionally, a decline in cycling can result in less government
funding for safe infrastructure. In this paper, we develop a method for
detecting the perceived perception of cyclists within news headlines. We
introduce a new dataset called ``Bike Frames'' to accomplish this. The dataset
consists of 31,480 news headlines and 1,500 annotations. Our focus is on
analyzing 11,385 headlines from the United States. We also introduce the
BikeFrame Chain-of-Code framework to predict cyclist perception, identify
accident-related headlines, and determine fault. This framework uses pseudocode
for precise logic and integrates news agency bias analysis for improved
predictions over traditional chain-of-thought reasoning in large language
models. Our method substantially outperforms other methods, and most
importantly, we find that incorporating news bias information substantially
impacts performance, improving the average F1 from .739 to .815. Finally, we
perform a comprehensive case study on US-based news headlines, finding
reporting differences between news agencies and cycling-specific websites as
well as differences in reporting depending on the gender of cyclists. WARNING:
This paper contains descriptions of accidents and death.
