论文标题
测量Twitter上的信念动态
Measuring Belief Dynamics on Twitter
论文作者
论文摘要
人们对错误信息和在线媒体在社会两极分化中的作用越来越关注。分析信念动态是增强我们对这些问题的理解的一种方法。现有的分析工具，例如调查研究或立场检测，缺乏将上下文因素与人口级别的信念动态变化相关的能力。在这项探索性研究中，我介绍了信念景观框架，该框架使用有关人们在在线环境中自称信念的数据来衡量具有高分辨率的信念动态。我通过将方法的输出与从文献中得出的一组假设进行比较，并检查该方法产生的“信念景观”，从而提供了对方法的初始验证。我的分析表明，该方法对不同的参数设置相对健壮，结果表明1）关于气候变化的两极分化问题，信念或吸引子的稳定配置和2）人们以这些吸引者的可预测方式移动。该方法为更强大的工具铺平了道路，该工具可用于了解现代数字媒体生态系统如何影响集体信念动态以及在该过程中发挥哪些作用误解。
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.
