Optimism about the Internet's potential to bring the world together has been
tempered by concerns about its role in inflaming the 'culture wars'. Via mass
selection into like-minded groups, online society may be becoming more
fragmented and polarized, particularly with respect to partisan differences.
However, our ability to measure the social makeup of online communities, and in
turn understand the social organization of online platforms, is limited by the
pseudonymous, unstructured, and large-scale nature of digital discussion. We
develop a neural embedding methodology to quantify the positioning of online
communities along social dimensions by leveraging large-scale patterns of
aggregate behaviour. Applying our methodology to 5.1B Reddit comments made in
10K communities over 14 years, we measure how the macroscale community
structure is organized with respect to age, gender, and U.S. political
partisanship. Examining political content, we find Reddit underwent a
significant polarization event around the 2016 U.S. presidential election, and
remained highly polarized for years afterward. Contrary to conventional wisdom,
however, individual-level polarization is rare; the system-level shift in 2016
was disproportionately driven by the arrival of new and newly political users.
Political polarization on Reddit is unrelated to previous activity on the
platform, and is instead temporally aligned with external events. We also
observe a stark ideological asymmetry, with the sharp increase in 2016 being
entirely attributable to changes in right-wing activity. Our methodology is
broadly applicable to the study of online interaction, and our findings have
implications for the design of online platforms, understanding the social
contexts of online behaviour, and quantifying the dynamics and mechanisms of
online polarization.

通过神经嵌入技术对 Reddit 14 年间 5.1B 评论的社区结构进行了研究，发现在 2016 年美国总统选举后，Reddit 出现了明显的政治极化，尤其是右翼用户的活动量增加导致的。个体级的极化是罕见的，政治极化可以通过考察单个用户的行为预测，可能因外部事件而发生。

在线平台上社会组织和政治极化的量化

Quantifying social organization and political polarization in online  platforms

We study the social structure of Facebook "friendship" networks at one
hundred American colleges and universities at a single point in time, and we
examine the roles of user attributes - gender, class year, major, high school,
and residence - at these institutions. We investigate the influence of common
attributes at the dyad level in terms of assortativity coefficients and
regression models. We then examine larger-scale groupings by detecting
communities algorithmically and comparing them to network partitions based on
the user characteristics. We thereby compare the relative importances of
different characteristics at different institutions, finding for example that
common high school is more important to the social organization of large
institutions and that the importance of common major varies significantly
between institutions. Our calculations illustrate how microscopic and
macroscopic perspectives give complementary insights on the social organization
at universities and suggest future studies to investigate such phenomena
further.

研究 Facebook 上百所美国大学的 “友谊” 网络的社会结构，并探讨用户属性在这些机构中的作用，比较不同特征在不同机构的相对重要性，可为深入研究此类现象提供微观和宏观视角的互补洞察。