Social media plays a central role in shaping public opinion and behavior, yet performing experiments on these platforms and, in particular, on feed algorithms is becoming increasingly challenging. This guide offers practical recommendations for researchers developing and deploying field experiments focused on real-time reranking of social media feeds. The article is organized around two contributions. First, we provide an overview of an experimental method using web browser extensions that intercepts and reranks content in real time, enabling naturalistic reranking field experiments. We then describe feed interventions and measurements that this paradigm enables on participants' actual feeds, without requiring the involvement of social media platforms. Second, we offer concrete technical recommendations for intercepting and reranking social media feeds with minimal user-facing delay, and provide an open-source implementation. This document aims to summarize lessons learned in running field experiments on social media, provide concrete implementation details, and foster the ecosystem of independent social media research. Finally, we release the source code that serves as a blueprint for implementing future feed-ranking experiments.

翻译：社交媒体在塑造公众观点与行为方面扮演着核心角色，然而在这些平台（尤其是信息流算法）上进行实验正变得日益困难。本指南为致力于开发和部署社交媒体信息流实时重排序实地实验的研究人员提供实用建议。本文围绕两项核心贡献展开：首先，我们概述了一种基于网页浏览器扩展的实验方法，该方法能实时拦截并重排序内容，从而实现自然情境下的信息流重排序实地实验。随后，我们描述了该范式在参与者实际信息流中可实施的干预措施与测量方法，整个过程无需社交媒体平台参与。其次，我们针对如何以最小化用户可感知延迟的方式拦截和重排序社交媒体信息流提出具体技术建议，并提供开源实现方案。本文旨在总结在社交媒体上开展实地实验的经验教训，提供具体实施细节，并促进独立社交媒体研究生态的发展。最后，我们公开了可作为未来信息流排序实验实施蓝本的源代码。