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

Qing Xiao; Rongyi Chen; Jingjia Xiao; Tianyang Fu; Alice Qian; Xianzhe Fan; Bingbing Zhang; Zhicong Lu; Hong Shen

doi:10.1145/3816914

Back

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

Journal article

Open access

Peer reviewed

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

Qing Xiao, Rongyi Chen, Jingjia Xiao, Tianyang Fu, Alice Qian, Xianzhe Fan, Bingbing Zhang, Zhicong Lu and Hong Shen

Proceedings of the ACM on human-computer interaction, Vol.10(6), pp.1-32

10/2026

DOI: 10.1145/3816914

Files and links (1)

url

https://doi.org/10.1145/3816914View

Published (Version of record) Open Access

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.

Empirical studies in collaborative and social computing

Human-centered computing

Details

Title: Subtitle: Constructing Algorithmic Authority: How Multi-Channel Networks (MCNs) Govern Live-Streaming Labor in China
Creators: Qing Xiao - Carnegie Mellon University
Rongyi Chen - Peking University
Jingjia Xiao - University of California San Diego
Tianyang Fu - The University of Texas at Austin
Alice Qian - Carnegie Mellon University
Xianzhe Fan - University of Hong Kong
Bingbing Zhang - University of Iowa
Zhicong Lu - George Mason University
Hong Shen - Carnegie Mellon University
Resource Type: Journal article
Publication Details: Proceedings of the ACM on human-computer interaction, Vol.10(6), pp.1-32
DOI: 10.1145/3816914
ISSN: 2573-0142
eISSN: 2573-0142
Publisher: ACM
Number of pages: 32
Alternative title: How Multi-Channel Networks (MCNs) Govern Live-Streaming Labor in China
Language: English
Date published: 10/2026
Academic Unit: Center for Social Science Innovation; School of Journalism and Mass Communication
Record Identifier: 9985236416602771

Metrics

1 Record Views