黄圣淳 / Shengchun Huang

黄圣淳 is the media and journalism scholar interviewed in 164.算法的“兔子洞”：为什么你总在看完新闻后滑向娱乐？｜对谈黄圣淳教授. The source identifies her as an assistant professor at the University of Texas at Austin journalism school and presents her work through questions about algorithmic recommendation, news distribution, information cocoons, and YouTube recommendation audits.

Her role in the wiki is to anchor the empirical media-effects branch of the algorithm series. She complicates simple Information Cocoon / 信息茧房 panic by emphasizing Incidental Exposure / 偶然暴露 and weak filter-bubble evidence, while also showing how Public Relevance Algorithms / 公共相关性的算法 can still shape civic attention through Algorithmic Amplification / 算法放大, Algorithmic Entertainment Redirect / 算法娱乐重定向, Platform Affordance / 平台可供性, and Platform Feedback Loop / 平台反馈循环.

Key Claims

• Algorithms should be studied as sociotechnical media systems, not as isolated technical objects.
• News production now passes through a recommendation and distribution layer that can become a new gatekeeper between editors and publics.
• Users remain active through clicks, skips, comments, search choices, and platform selection, but platforms preselect what choices become available.
• The most consequential algorithmic harms may include affective friction, entertainment drift, distorted perception of public opinion, and unconscious feedback loops rather than only ideological isolation.

Connections

• [[QizhulouYanBinke|起朱楼宴宾客]] and [[DavidWeng|大卫翁]] — interview context.
• Public Relevance Algorithms / 公共相关性的算法, Information Cocoon / 信息茧房, Filter Bubble / 过滤气泡, and Incidental Exposure / 偶然暴露 — research branch she helps clarify.
• YouTube, Algorithmic Entertainment Redirect / 算法娱乐重定向, and Algorithmic Amplification / 算法放大 — recommendation-audit and media-effects branch.
• Platform Affordance / 平台可供性, Platform Feedback Loop / 平台反馈循环, and Algorithmic Media Literacy / 算法媒介素养 — user and creator practice branch.