  There is growing concern about misinformation and the role online media plays
in social polarization. Analyzing belief dynamics is one way to enhance our
understanding of these problems. Existing analytical tools, such as survey
research or stance detection, lack the power to correlate contextual factors
with population-level changes in belief dynamics. In this exploratory study, I
present the Belief Landscape Framework, which uses data about people's
professed beliefs in an online setting to measure belief dynamics with high
resolution. I provide initial validation of the approach by comparing the
method's output to a set of hypotheses drawn from the literature and by
inspecting the "belief landscape" generated by the method. My analysis
indicates that the method is relatively robust to different parameter settings,
and results suggest that 1) there are many stable configurations of belief, or
attractors, on the polarizing issue of climate change and 2) that people move
in predictable ways around these attractors. The method paves the way for more
powerful tools that can be used to understand how the modern digital media
ecosystem impacts collective belief dynamics and what role misinformation plays
in that process.
