Constructing Algorithmic Authority: How Multi-Channel Networks (MCNs) Govern Live-Streaming Labor in China

TL;DR

This paper explores how Chinese MCNs shape live-streaming labor through discursive construction of platform algorithms, influencing streamer behavior and labor management practices.

Contribution

It reveals how MCNs produce and circulate algorithmic interpretations that serve as soft control mechanisms in platform labor governance.

Findings

MCNs interpret algorithms as volatile and uncertain, adopting probabilistic management strategies.

MCNs circulate simplified narratives framing algorithms as transparent and fair.

Streamers internalize these narratives, increasing self-discipline and investment in their work.

Abstract

This study examines the discursive construction of algorithms and its role in labor management in Chinese live-streaming industry by focusing on how intermediary organizations (Multi-Channel Networks, MCNs) actively construct, stabilize, and deploy particular interpretations of platform algorithms as instruments of labor management. Drawing on a nine-month ethnographic fieldwork and 44 interviews with live-streamers, former live-streamers, and MCN staff, we examine how MCNs produce and circulate structured interpretations of platform algorithms across organizational settings. We show that MCNs articulate two asymmetric yet interconnected forms of algorithmic interpretations. Internally, MCNs managers approach algorithms as volatile and uncertain systems and adopt probabilistic strategies to manage performance and risk. Externally, in interactions with streamers, MCNs circulate simplified and prescriptive algorithmic narratives that frame platform systems as transparent, fair, and responsive to individual effort. These organizationally produced algorithmic interpretations are embedded into training materials, live-streaming performance metrics, and everyday management practices. Through these mechanisms, streamers internalize responsibility for outcomes, intensify self-discipline, and increase investments in equipment, performing skills, and routines to maintain streamer-audience relationship, while accountability for unpredictable outcomes is increasingly shifted away from managers and platforms. This study contributes to CSCW and platform labor research by demonstrating how discursively constructed algorithmic knowledge can function as an intermediary infrastructure of soft control, shaping how platform labor is regulated, moralized, and governed in practice.