论文标题
量化在线平台中的社会组织和政治两极分化
Quantifying social organization and political polarization in online platforms
论文作者
论文摘要
人们对互联网将世界融合在一起的潜力的乐观情绪受到对其在激发“文化战争”中的作用的担忧。通过大规模选择志同道合的团体，在线社会可能会变得越来越分散和两极分化，尤其是在党派差异方面。但是，我们衡量在线社区的社会构成的能力，进而了解在线平台的社会组织，受到数字讨论的假名，非结构化和大规模的性质的限制。我们开发了一种神经嵌入方法，通过利用大规模的总体行为模式来量化在线社区沿着社会维度的定位。将我们的方法应用于14年来10K社区的5.1b Reddit评论，我们衡量了如何根据年龄，性别和美国政治党派组织宏观社区结构。在研究政治内容时，我们发现Reddit在2016年美国总统大选中发生了重大的两极分化事件，此后多年以来一直高度两极化。然而，与传统的智慧相反，个人级别的极化很少见。 2016年的系统级别的转变是由新的和新政治用户的到来的不成比例的。 Reddit上的政治两极分化与该平台上的先前活动无关，而与外部事件保持一致。我们还观察到一个鲜明的意识形态不对称性，2016年的急剧增加完全归因于右翼活动的变化。我们的方法广泛适用于在线互动的研究，我们的发现对在线平台的设计，了解在线行为的社会环境以及量化在线两极分化的动态和机制具有影响。
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.
