Designed a Twitter spam moderation workbench that increased moderator efficiency by 22% while improving the quality of data used to train machine learning models

Diving into the Requirements

As the sole designer on the project I needed to get a clear picture of the workbench rules and how moderators flowed through the Spam Violation annotation process. First, I parsed through multiple documents to condense the workbench ruleset into a concise list of requirements.

Analyzing Real User Behavior

The Twitter team shared dozens of screen-capture videos showing moderators working in real time. These recordings became an invaluable source of insight for the project. The videos revealed several pain points that slowed moderators down. They relied on multiple browser tabs to review tweets, check user accounts, search for keywords, and translate text. Much of their time was spent copying and pasting information between tabs.

This work operated under tight operational constraints. Teams processed large volumes of data daily, accuracy requirements were high, and mistakes carried downstream cost. Any design change had to support speed without sacrificing precision or reviewer confidence.

We brought these findings back to the Twitter team to confirm their accuracy and aligned on the opportunity to address them as a way to improve SAR scores.

Mapping the Moderator Experience

To understand where friction and cognitive load were accumulating, I mapped the end-to-end moderator experience across annotation, review, and decision points. The map revealed breakdowns in flow, loss of context, and key moments where the system needed to better support speed, accuracy, and confident human judgment.

Collaborative Ideation

Including the Twitter team in every step of the design process was very important. We brought together key stakeholders and moderators for a "What If" exercise allowing us to go wide with our thinking.

Design and Iteration

Based on what we learned from research and ideation, it was clear that moderators needed all of their key data points brought directly into the app. Switching between multiple browser tabs was slowing them down. They also needed simple tools to translate and copy text. I created a series of mockups exploring different layouts and interactions, then met with the team again to validate the direction.