"Don't Mess Up My Algorithm": Phatic Communication and Algorithmic Contagion in Meme Sharing

TL;DR

This paper explores how meme exchanges via direct messages on Instagram serve as relational communication and how users perceive their influence on personalized recommendations, revealing tensions between social practices and algorithmic control.

Contribution

It provides empirical insights into user perceptions of DM meme sharing and proposes design strategies for more transparent and user-controlled recommendation systems.

Findings

Users see meme sharing as relational and interpret unfriendly memes as 'algorithmic contagion'

Users feel powerless due to opaque recommendation linkages and limited control options

Design implications include transparency, opt-outs, and conservative learning approaches

Abstract

On algorithmic social platforms, exchanging memes via direct messages (DMs) serves as phatic communication that affirms relationships, yet users often interpret these exchanges as signals shaping personalized recommendations, creating tension between relational practice and algorithmic control. This study examines how users perceive DM meme exchanges on Instagram rather than auditing Instagram's underlying recommender mechanisms, and how beliefs about DM-recommendation linkages shape coping strategies and feelings of powerlessness. We conducted semi-structured interviews with 21 active meme-DM users. Participants classified memes as recipient-friendly or recipient-unfriendly based on relational fit; many described the spread of unfriendly memes as "algorithmic contagion." Controls were constrained by relational norms, low perceived efficacy of feedback tools, and opaque DM-recommendation linkages. We articulate how DM-based relational practices are entangled with personalization infrastructures and propose three design implications: transparent linkage explanations, conversation-level opt-outs, and conservative learning that down-weights DM-originated signals.