Java source code to repeat the experiment;Weibo data collected by the experiment from Endogenetic structure of filter bubble in social networks

The filter bubble is an intermediate structure to provoke polarization and echo chamber in social network, and it has become one of the most urgent issues for social media of the time. Previous studies usually equated filter bubbles with community structures and emphasized this exogenous isolation effect, but there is a lack of full discussion of the internal organization of filter bubbles. Here, we design an experiment for analysing filter bubbles taking advantage of social bots. We deployed 128 bots to Weibo (the largest microblogging network in China), and each bot consumed a specific topic (entertainment or sci-tech) and ran for at least two months. In total, we recorded about 1.3 million messages exposed to these bots and their social networks. By analysing the text received by the bots and motifs in their social networks, we found that a filter bubble is not only a dense community of users with the same preferences but also presents an endogenetic unidirectional star-like structure. The structure could spontaneously exclude non-preferred information and cause polarization. Moreover, our work proved that the felicitous use of artificial intelligence technology could provide an useful experimental approach that combines privacy protection and controllability in studying social media.

过滤气泡（filter bubble）是一种会引发社交网络极化现象与回声室效应的中间结构，现已成为当下社交媒体领域亟待解决的核心议题之一。既往研究通常将过滤气泡等同于社群结构，并着重强调其外源性隔离效应，但却鲜有针对过滤气泡内部组织架构的完整探讨。本研究借助社交机器人（social bots）设计了一项用于分析过滤气泡的实验：我们在中文最大的微博（Weibo）平台部署了128个社交机器人，每个机器人定向关注特定主题（娱乐或科技）并持续运行至少两个月。总计，我们记录了约130万条被这些机器人及其社交网络所接收的信息。通过分析机器人接收的文本内容及其社交网络中的网络基序（motifs），我们发现：过滤气泡不仅是由偏好趋同用户构成的高密度社群，同时还呈现出一种内源性单向星型结构。该结构可自发排斥非偏好性信息并引发极化现象。此外，本研究证实，合理运用人工智能技术能够为社交媒体研究提供一种兼顾隐私保护与可控性的有效实验路径。