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Let's Influence Algorithms Together: How Millions of Fans Build Collective Understanding of Algorithms and Organize Coordinated Algorithmic Actions

Qing Xiao, Yuhang Zheng, Xianzhe Fan, Bingbing Zhang, Zhicong Lu

TL;DR

The paper addresses how millions of fans collectively understand and act on platform algorithms, extending folk theories of algorithms from individuals to large-scale collectives. It employs a two-year ethnography of 43 core fans and 15 general fans across multiple platforms and countries to dissect motivation, learning, and organization of collective actions aimed at influencing algorithms. Findings show that core fans decode and teach algorithmic practices through simple tutorials, mobilize general fans with emotionally charged rhetoric, and coordinate actions across platforms, achieving large-scale impact while highlighting governance and ethical considerations. By integrating ethnography with CSCW theory, the study advances understanding of how grassroots, computer-supported collective actions can reshape algorithmic outputs and platform governance, offering design principles for fairness, transparency, and resilience in social platforms.

Abstract

Previous research pays attention to how users strategically understand and consciously interact with algorithms but mainly focuses on an individual level, making it difficult to explore how users within communities could develop a collective understanding of algorithms and organize collective algorithmic actions. Through a two-year ethnography of online fan activities, this study investigates 43 core fans who always organize large-scale fans collective actions and their corresponding general fan groups. This study aims to reveal how these core fans mobilize millions of general fans through collective algorithmic actions. These core fans reported the rhetorical strategies used to persuade general fans, the steps taken to build a collective understanding of algorithms, and the collaborative processes that adapt collective actions across platforms and cultures. Our findings highlight the key factors that enable computer-supported collective algorithmic actions and extend collective action research into the large-scale domain targeting algorithms.

Let's Influence Algorithms Together: How Millions of Fans Build Collective Understanding of Algorithms and Organize Coordinated Algorithmic Actions

TL;DR

The paper addresses how millions of fans collectively understand and act on platform algorithms, extending folk theories of algorithms from individuals to large-scale collectives. It employs a two-year ethnography of 43 core fans and 15 general fans across multiple platforms and countries to dissect motivation, learning, and organization of collective actions aimed at influencing algorithms. Findings show that core fans decode and teach algorithmic practices through simple tutorials, mobilize general fans with emotionally charged rhetoric, and coordinate actions across platforms, achieving large-scale impact while highlighting governance and ethical considerations. By integrating ethnography with CSCW theory, the study advances understanding of how grassroots, computer-supported collective actions can reshape algorithmic outputs and platform governance, offering design principles for fairness, transparency, and resilience in social platforms.

Abstract

Previous research pays attention to how users strategically understand and consciously interact with algorithms but mainly focuses on an individual level, making it difficult to explore how users within communities could develop a collective understanding of algorithms and organize collective algorithmic actions. Through a two-year ethnography of online fan activities, this study investigates 43 core fans who always organize large-scale fans collective actions and their corresponding general fan groups. This study aims to reveal how these core fans mobilize millions of general fans through collective algorithmic actions. These core fans reported the rhetorical strategies used to persuade general fans, the steps taken to build a collective understanding of algorithms, and the collaborative processes that adapt collective actions across platforms and cultures. Our findings highlight the key factors that enable computer-supported collective algorithmic actions and extend collective action research into the large-scale domain targeting algorithms.
Paper Structure (35 sections, 1 figure, 2 tables)

This paper contains 35 sections, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Folk Theories of Algorithm
  4. Computer-Supported Collective Action
  5. Transformative Fandom
  6. Method
  7. Ethnography Context: Define "Core Fans" and "General Fans"
  8. Participating Role
  9. Participants: Core Fans (N=43) and General Fans (N=15)
  10. Data Collection
  11. Online and on-site observation
  12. Interviews with core fans and general fans
  13. Focus groups with core fans
  14. Data Analysis
  15. Positionality Statement
  16. ...and 20 more sections

Figures (1)

  • Figure 1: The figure shows a step-by-step tutorial created by fans of Xiao Zhan on how to make multiple reservations for his new drama Cang Hai Zhuan on Weibo's drama rating platform (similar to a Chinese version of IMDb). After making a reservation, the platform notifies users when the drama airs, so the number of reservations reflects public anticipation for the drama. Generally, each user can only make one reservation. Therefore, fans often register multiple accounts to make additional reservations.