论文标题
wisdom_of_crowds：一种高效，哲学上验证的社会认识论网络分析工具包
The wisdom_of_crowds: an efficient, philosophically-validated, social epistemological network profiling toolkit
论文作者
论文摘要
代理商的认知位置通常取决于它们在向其提供信息的其他代理网络中的位置。总的来说，如果代理人拥有多样化和独立的来源，他们会更好。 Sullivan等。 [2020]开发了一种定量表征个体在网络中的认知位置的方法，该方法考虑了多样性和独立性。并在从Twitter数据得出的小图上提出了概念验证，封闭式实现[Sullivan等。 2020]。本文报告了其在Python中其算法的开源重新实现，并在更大的网络上进行了优化。除了算法和软件包外，我们还展示了将包装扩展到大型合成社交网络图谱分析的能力，并最终证明了其在在线社交媒体上分析“ Echo Chambers”现实世界经验证据的实用性，以及在学术通信网络中跨学科多样性的证据。
The epistemic position of an agent often depends on their position in a larger network of other agents who provide them with information. In general, agents are better off if they have diverse and independent sources. Sullivan et al. [2020] developed a method for quantitatively characterizing the epistemic position of individuals in a network that takes into account both diversity and independence; and presented a proof-of-concept, closed-source implementation on a small graph derived from Twitter data [Sullivan et al. 2020]. This paper reports on an open-source re-implementation of their algorithm in Python, optimized to be usable on much larger networks. In addition to the algorithm and package, we also show the ability to scale up our package to large synthetic social network graph profiling, and finally demonstrate its utility in analyzing real-world empirical evidence of `echo chambers' on online social media, as well as evidence of interdisciplinary diversity in an academic communications network.
