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 article offers practical recommendations to researchers developing and deploying field experiments focused on real-time re-ranking of social media feeds. This article is organized around two contributions. First, we overview an experimental method using web browser extensions that intercepts and re-ranks content in real-time, enabling naturalistic re-ranking 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 re-ranking social media feeds with minimal user-facing delay, and provide an open-source implementation. This document aims to summarize lessons learned, provide concrete implementation details, and foster the ecosystem of independent social media research.

翻译：社交媒体在塑造公众舆论与行为方面发挥着核心作用，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。本文为致力于开发和部署社交媒体信息流实时重排序现场实验的研究人员提供实用建议。本文围绕两项贡献展开：首先，我们概述了一种利用网页浏览器扩展的实验方法，该方法可实时拦截并重排序内容，从而实现自然主义的信息流重排序现场实验。随后，我们描述了该范式能够在参与者实际信息流上实施的干预措施与测量方法，且无需社交媒体平台的参与。其次，我们为以最小用户可感知延迟实现社交媒体信息流拦截与重排序提供了具体技术建议，并提供了开源实现。本文旨在总结实践经验、提供具体实施细节，并促进独立社交媒体研究生态体系的发展。