In this blog, we share how we adopted the CausalImpact package from Google on a Twitter edge network experiment to measure the impact of improving Twitter's latency on customer engagement and revenue.

In this blog, we discuss how we power real-time search for Tweets, Users, Direct Messages and more using Elasticsearch.

In this post, we share how Twitter implemented a Data Quality Platform that gave customers the ability to have automated data quality checks.

In this blog post, we discuss the scale at which Twitter operates through Project Sparrow, an initiative that shifted the architecture of our data pipelines from a batch event approach to streaming.

Over-squashing is a common plight of Graph Neural Networks. In this post, we discuss how this phenomenon can be understood and remedied through the concept of Ricci curvature.

In this blog, we share findings from a follow-up analysis in which we seek to understand how prompts cause people to reconsider potentially harmful or offensive content before they hit send.

In this blog, we discuss how we approached migrating an exabyte of data to Google Cloud to make it easier for our Tweeps to analyze and visualize data.

In this blog, we share an overview of Twitter’s Druid ecosystem and discuss our work towards a unified ingestion experience.

On the Data Science team, a piece of our job is understanding how conversations are happening on Twitter. In this blog, we use Wordle as a case study to showcase how we think about similar analyses.

This blog post discusses how feature propagation can be an efficient and scalable approach for handling missing features in graph machine learning applications.