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.
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:
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.
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.
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.
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 are relatively simple, subtle improvements backed by research and testing had a significant positive impact on the user experience.
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, Musical.ly, 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
For more details about Two Hat Security's Community Sift product visit the Community Sift website.
For more details about this project view the Community Sift Portfolio PDF.