Social Media Feed Ranking Algorithms: Guide to Field Experiments

Reranking partisan animosity in algorithmic social media feeds alters affective polarization

Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms. We developed a platform-independent method that reranks participants’ feeds in real time and used this method to conduct a preregistered 10-day field ...

Understanding Social Media Recommendation Algorithms

Access the PDF version of this essay by clicking the icon to the right.

From Feeds to Trails

To design the future of social media, rethink the interface before the algorithm.

How the Twitter/X Algorithm Works in 2026 (Source Code)

Twitter published its ranking weights on GitHub. A reply is worth 150x a like. Free accounts get 10x less reach. Full scoring system explained.

Platform Stats — blogs.social

Discover and follow blogs across Bluesky, Mastodon, RSS, and the open social web.

Algorithmic Choice with Custom Feeds - Bluesky

Our implementation of algorithmic choice lets users customize one of the most important parts of their social media experience: their feed.

Paper Notes - WTF - The Who to Follow Service at Twitter

Explore Twitter's recommendation system - in-memory graph processing with Cassovary, random walks, and SALSA for user suggestions.

Bridging-Based Ranking

There is significant concern about the engagement-based ranking systems used by TikTok, Facebook, YouTube, etc. to recommend content. Bridging-based ranking systems can address one of the most dangerous aspects of such algorithmic recommendations—the push toward polarization and divisiveness that is tearing nations apart—and do so without reducing anonymity or increasing censorship. This report explores what bridging-based ranking is, how it helps (overcoming downsides of chronological feeds and middleware), addresses common objections, and provides early examples of its use and benefits in the wild. The report concludes by providing next steps for platforms, governments, funders, and researchers in order to accelerate the deployment of bridging.

Twitter's For You Recommendation Algorithm

Twitter has open-sourced a majority of its recommendation algorithm. It offers an exciting opportunity for researchers, industry practitioners, and RecSys enthusiasts to take a close look at how Twitter computes the recommended feed for the For You page. This article described Twitter’s end-to-end recommender system along with relevant literature and code references.

How Do Bluesky Feeds Work?

Explore how Bluesky's unique feeds work, including Custom, Following, and Discover feeds, for a tailored social media experience.

Paper Skygest: Personalized Academic Recommendations on Bluesky

We build, deploy, and evaluate Paper Skygest, a custom personalized social feed for scientific content posted by a user's network on Bluesky and the AT Protocol. We leverage a new capability on emerging decentralized social media platforms: the ability for anyone to build and deploy feeds for other users, to use just as they would a native platform-built feed. To our knowledge, Paper Skygest is the first and largest such continuously deployed personalized social media feed by academics, with over 50,000 weekly uses by over 1,000 daily active users, all organically acquired. First, we quantitatively and qualitatively evaluate Paper Skygest usage, showing that it has sustained usage and satisfies users; we further show adoption of Paper Skygest increases a user's interactions with posts about research, and how interaction rates change as a function of post order. Second, we share our full code and describe our system architecture, to support other academics in building and deploying such feeds sustainably. Third, we overview the potential of custom feeds such as Paper Skygest for studying algorithm designs, building for user agency, and running recommender system experiments with organic users without partnering with a centralized platform.