"The Filter Bubble Effect of Algorithmic Ranking on Social Media" by Ms. Kayla Guangrui Li
- 1:00 p.m. — 2:30 p.m.
Ms. Kayla Guangrui Li
Ph.D. Candidate in Information Systems
Department of Information Systems
Hong Kong University of Science and Technology
Although some argue that personalization algorithms can help to deal with information overload and improve user experience, they can also induce algorithmic bias that could skew or limit the information an individual user sees on the Internet, thus creating the ‘filter bubble’ phenomenon. This study assesses the prevalence of filter bubble on social media, and especially its long-term impact, by making use of a quasi-experiment on two leading social media platforms in China. We leveraged our unique dataset to carry out a difference-in-difference analysis by comparing users’ behavioral changes on Sina and Tencent Weibo. Our findings suggested that after the implementation of ranking algorithms, people’s interest scope became much limited, and this effect was magnified over time. We also found evidence that people encountered less attitude-challenging content after the implementation of ranking algorithms, as shown by their posts becoming less negative in tone. Together, these findings provide strong evidence for the existence of filter bubble caused by ranking algorithm. We further examined the impact of filter bubble on users’ contribution behavior to the platforms and found that the filter bubbles significantly reduced the number of posts generated by users, both reposted and original. Our findings have implications for improved transparency in platform design and for governance of user-generated content.