Can Robots Feel "Pain" ? Discourse Construction and Public Meaning Production in Short‑Video Platform Comment Interactions - Ranran Zeng (Social Sciences and Humanities, 2026)
Existing research on robot abuse and mind attribution has largely employed experimental methods focusing on individual responses, with less attention to how such responses continuously diffuse into public meaning through real platform interactions. This study takes the stability test video of Unitree's G1 humanoid robot on Douyin as a focal case, and uses large language model‑assisted inductive qualitative coding to analyze highly‑liked comments and their secondary replies. The findings reveal that although the video itself presents a technical performance test, the meaning‑making in the comment section almost completely bypasses the technical dimension. Commenters, through self‑reference, associate the robot with their own childhood, family, and other life experiences, endowing it with subjective experiential capacity beyond its technical attributes; among these, the interaction chain framed by childhood experiences is the largest and most influential. Concurrently, existing public discourse resources on the platform, such as school bullying, elder abuse, and animal protection, are repeatedly invoked and grafted onto the robot, elevating individual emotions into a public victim narrative. Based on these findings, this study proposes a three‑layer analytical framework of "video presentation—comment interaction—public discourse," revealing that the robot's victim identity does not originate from its own properties or the video content, but is collectively produced by commenters through self‑experience projection and reuse of existing discourses in ongoing interactions.