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.

翻译：在算法社交平台上，通过直接消息（DM）交换模因是一种维系关系的寒暄交流，然而用户常将这些交换解读为塑造个性化推荐的信号，从而在关系实践与算法控制之间产生张力。本研究考察用户如何感知Instagram上DM模因交换（而非审计Instagram底层推荐机制），以及关于DM-推荐关联性的信念如何塑造应对策略与无力感。我们对21名活跃的DM模因使用者进行了半结构化访谈。参与者根据关系契合度将模因分类为接收者友好型与接收者不友好型；许多人将不友好模因的传播描述为“算法传染”。控制行为受到关系规范、反馈工具感知效能低下以及DM-推荐关联性不透明的制约。我们阐明了基于DM的关系实践如何与个性化基础设施相互纠缠，并提出三项设计启示：透明的关联解释、对话级别退出机制，以及降低DM来源信号权重的保守学习策略。