Community Sift

User Experience Design
User Interface Design


Community Sift is Two Hat Security's enterprise SaaS product excelling at content moderation using artificial intelligence to give clients insights into their data.


Community Sift already had layouts for text queues and image queues. I was asked to assess these queues and to make UX adjustments that would provide a better experience.


Text queues and image queues are a part of the Community Sift tools where clients have their staff of moderators scan through the most egregious or questionable text and image content created by their users. Content arrives as text lines or a series of images in a paginated queue of items ordered according to priority.

Community Sift Moderation Flow


The first step involved working with key clients to perform usability testing and get user feedback in order to understand how the queues were being used, the pain points, and noting user observations. Post-it notes and sketches were used to help create a preliminary understanding of how the main elements relate to each other with reference to the ideas drawn from ongoing research.

One key discovery made was organizing how the information was laid out on the page into four main categories:

  • Risk Assessment
  • User Information
  • Content
  • Moderation Actions
Community Sift Queue Analysis Community Sift Queue Analysis Four Categories


Time was spent interviewing the users. Speed was considered the most important issue. The faster moderators can identify and remove unwanted content the faster their community will be a better place. And a positive community means a better user experience for their users and a better ROI for the business.


  • Most of the time moderators (users of the queues) are reading text or scanning for images, as opposed to performing actions
  • Speed is determined by ability of moderators to quickly read the text and the legibility of the layout
  • How do we as humans read text or scan?
  • What are the mechanisms for reading and scanning accurately and quickly?
  • Can I leverage those characteristics?.


Research was done on legibility (ideal typographic measure), eye tracking, and speed-reading. Research included studying papers already written as well as creating and conducting in-house tests to substantiate research and explore details unique to the Community Sift product.

Reading Fixations Saccades and Eye Tracking

LEFT: An example of fixations and saccades over text. The eyes never move smoothly over still text. Reading Fixations Saccades by Lucs-kho

RIGHT: Yarbus eye tracker from the 1960s. Yarbus eye tracker by Yarbus, A. L.


The main objective was to organize text in the most legible placement for quick eye tracking. The multiple text inputs were placed closer together vertically so the text to be evaluated read as much as possible as one group of text in a column.

The secondary objectives were to reduce all other visual distractions such as reducing the risk rating size and providing separation between user name text and the content text.

Community Sift Queue Wireframes


Client’s moderators using the new text queues were able to improve their moderation speed up to twice as fast as before. The moderators were also able to be more accurate. Client feedback included positive comments about the improved functionality of the new layout as well as the benefits of less scanning distance between content and moderator actions. Although the changes were relatively simple, subtle improvements backed by research and testing had a significant positive impact on the user experience.

Community Sift Text Queue Explanation Community Sift Text Queue Community Sift Text Queue


Various projects were worked on while at Two Hat Security. One major project was the redesign of the SaaS enterprise product Community Sift.


The goal was to allow the user to get a quick overview of the health of their communities by displaying some key statistics. The navigation buttons also contain statistics and notifications for a quick preview and indication of what's happening in each area of the Community Sift tools.

Community Sift Text Post-it Notes Community Sift Mobile Wireframe Community Sift Desktop Wireframe Community Sift Dashboard


Mike worked directly with Community Sift clients to listen to issues, have a dialogue, and discover solutions. Mike was honoured to have worked with such amazing clients as Habbo,, Medium, WoozWorld, Roblox, SuperAwesome (PopJam), Kabam (Marvel), Storybird, and others.

The quotes are feedback from our clients about their experiences using the Community Sift app.

With Sift we have effectively reduced moderation, with about 70% of mod workload having been reduced...this is giving time back to our mods who are able to engage with the community and otherwise focus on the user experience.”

- Giorgo Paizanis, VP of Habbo

The success of our daily operations depends upon our relationship with Two Hat Security. We have an excellent working relationship with them and know they’ll respond to any problems within minutes. They’re like fine waiters, there when you need them with exactly what you need!”

- Rebecca Newton, Head of Trust & Community of SuperAwesome

Commmunity Sift Logo


For more details about Two Hat Security's Community Sift product visit the Community Sift website.

For more details about this project and to see higher resolution images, view the Community Sift Portfolio PDF.