Today marks a new era of transparency for Twitter. 🧵
We’re sharing much of the source code that powers our platform with the world. Visit our blog to learn more about this initiative: blog.twitter.com/en_us/topics/c…
The real magic of Twitter is in our recommendations algorithm, which powers the hit Tweets you see in your For You timeline. We broke down how it all works here: blog.twitter.com/engineering/en…
Our Aggregation Framework is a config-driven Summingbird based framework for generating real-time and batch aggregate features to be consumed by ML models.
User Signal Service (USS) is a centralized online platform that supplies comprehensive data on user actions and behaviors on Twitter. This service stores information on both explicit signals, such as Favorites, Retweets, and replies, and implicit signals like Tweet clicks,… twitter.com/i/web/status/1…
Unified User Action (UUA) is a centralized, real-time stream of user actions on Twitter, consumed by various product, ML, and marketing teams. UUA makes sure all internal teams consume the uniformed user actions data in an accurate and fast way.
Interview pro-tip: To those interviewing for our engineering roles - checkout some of these key blog posts that can help you understand our architecture and prepare for the System Design rounds. 1/5
You may have heard about this year's Economics Nobel Prize winners - David Card, Josh Angrist (@metrics52) & Guido Imbens.
Their publicly available work has helped us solve tough problems @Twitter, and we're excited to celebrate by sharing how their findings have inspired us.
Understanding causal relationships is core to our work on identifying growth opportunities and measuring impact.
This year's winners laid the foundation for cutting-edge techniques we use to understand where Twitter can improve and how changes affect our platform experience.
To share a few exciting causal inference applications at Twitter:
In our latest blog post, we’re sharing the findings from our image cropping algorithm analysis and exploring ways to create a more equitable experience on Twitter.
As part of our commitment to transparency, we’ve also published our analysis on ArXiv and are sharing our source code so you can reproduce and better our analysis.
Inclusive language plays a critical role in fostering an environment where everyone belongs. At Twitter, the language we have been using in our code does not reflect our values as a company or represent the people we serve. We want to change that. #WordsMatter
We’re starting with a set of words we want to move away from using in favor of more inclusive language, such as:
There is no switch we can flip to make these changes everywhere, at once. We will continue to iterate on this work and want to put in place processes and systems that will allow us to apply these changes at scale. We’re focusing on these areas 👇