savolainen-2022-the-shadow-banning-controversy-perceived-governance-and-algorithmic-folklore

Abstract

• Definition of platform governance through algorithmic folklore.

• Focus on user discussions regarding 'shadow banning'—a form of content moderation alleged by users but denied by platforms.

• Methodological implications for studying algorithmic governance—how users generate expectations and cope with perceived injustices.

• Asserted tension between platform governance practices and ideal governance values (clarity, consistency).

Introduction

• User experiences of shadow banning characterized by claims of ideological censorship.

• Shadow banning defined as moderation limiting visibility without user awareness—a practice reportedly coined in 2001.

• Public awareness increased in 2018 as accusations of bias arose against platforms like Twitter.

• Platforms deny use of shadow banning; accusations often correspond to broader debates on content moderation.

Shadow Banning Defined

• Historical Context: Coined in 2001, first seen in forums as a way to hide posts from visibility while the user remains unaware.

• Controversy: Definitions vary, often conflating with notions of content suppression; prominent in political discourse.

• Perceived Implications: Claims of shadow banning raise concerns about user rights within platform governance frameworks.

Algorithmic Governance

• Defined as systems utilizing automated tools to manage user interactions and content—subject to inconsistencies and opacity.

• Moderation Practices: Considered critical to platform identity, enabling regulatory tasks yet simultaneously cultivating user frustration.

• The challenge of accountability arises from a lack of transparency in content screening—reflecting asymmetries in power dynamics.

User Engagement and Experiences

• Users often share beliefs and narratives about algorithmic behavior—termed algorithmic folklore.

• Users report experiences of perceived shadow bans shaped by emotions, expectations, and community narratives.

• Emotions drive user discussions, revealing patterns in expectations about visibility and performance metrics.

• Narratives of Control: Users frustrated by opaque policies create speculative theories about algorithm motives and behavior.

Findings on Users’ Narratives and Beliefs

• Folk Theories: Users develop informal theories regarding moderation, often seeing themselves as 'damned' by hidden rules—illustrated by anecdotal evidence.

• Common beliefs relate to hashtag usage and content characteristics, influencing user behavior in attempts to align with perceived algorithms.

• The perception that algorithms enforce hidden guidelines contributes to users crafting complex narratives about visibility and engagement outcomes.

• Algorithmic Effects: Users express feelings of powerlessness as they navigate a system that they perceive to be governed by arbitrary decisions.

Conclusion

• Revealing Governance Issues: Shadow banning folklore reflects users' struggle with and against platform governance—raising questions about accountability.

• The disconnect between user experience and platform practice highlights ongoing tensions around algorithmic decision-making and transparency.

• Users emerge as active but frustrated participants in a system that remains unresponsive to their concerns, indicating a troubling dynamic for regulatory governance of online platforms.

• Broader Implications: Calls for transparency and the ethical reconfiguration of algorithmic governance challenge platforms’ legitimacy in operationalizing democratic values.