The Double-Edged Sword of Socially-Produced Propaganda: Analyzing the "Kill Line" Narrative on Chinese Social Media

• In Event: Communication, Information Technologies, and Media Sociology Section Roundtables
  In Refereed Roundtable (60 minute): 6. Presentation, propaganda, & politicization

Abstract

In late 2025, Chinese social media became obsessed with American poverty. Adapted videogame terminology, the “kill line” (斩杀线) narrative framed U.S. economic insecurity as an “execute threshold” beyond which small shocks can trigger a cascading downward spiral. The framing is widely attributed to “LaoA” (牢A), a Chinese international student whose December 2025 livestream went viral and received eventual institutional amplification when official CCP outlets invoked the term to condemn American capitalism. This paper argues that socially produced nationalism is a double-edged propaganda resource: its social origins can lend authenticity, yet the same social embeddedness exposes it to credibility contestation and rapid peer verification—especially by users with diasporic experience and platformed information-sharing capacities. To assess how peer verification and credibility contest shape the reception of state-amplified, socially originated propaganda, I analyze Zhihu (a Chinese Q&A platform) discourse around the “Kill Line” narrative, scraping answers and nested comments from questions tagged “American Kill Line,” “LaoA,” “American Poverty,” and “American Homelessness,” alongside metadata including upvotes, comment counts, user IDs, and platform-displayed IP location. Following a discovery-first workflow, I use topic modeling to map discursive space and iteratively develop a two-layer codebook—stance (supportive/critical) and narrative strategy—which is then scaled via LLM-assisted classification validated against a hand-coded gold standard. Finally, I model how answer stance/strategy predicts the composition of downstream comment types using a Dirichlet–multinomial regression with question fixed effects and interactions with IP-location to test the diaspora/verification mechanism.

Author

• Yunhan Wen, Princeton University