Estimating the popularity of content is not an easy task, especially when we know that attractiveness is subjective and mostly in the eye of the beholder. There are various reasons why we might be interested in popular items, especially when they are in a subject close to our heart or related to what we already do. Such reasons include learning from the best examples, learning about new people who are the best in their field, learning which work is worth reviewing without spending too much time on every new published piece. We could also notice new trends or learn to anticipate what could be valued in the future.
Exploring Dribble data
Dribbble is a place where graphic/UI designers and animators present their work and get feedback on it. Many people are willing to help and as long as someone is open to criticism, they can learn how to improve the quality of their work. The site has a dedicated section for job offers, which is another plus for both beginners and qualified designers.
The site is already well-established in the design community and exists from a long time. Here we will concentrate on Dribbble shots sorted by the criteria "popular" and "all time". This is how we could obtain information about the first 600 of the most popular items since the introduction of the site. This gives us a limited amount of data we could explore, but it is still useful. Here you can see the results from the clustering with the DBSCAN algorithm given the numbers of views, comments and hearts received.
In the lower left part of the diagram we see many items colored with dark orange, which indicates that they belong to the same cluster. This is the cluster with the most items in it. Since we want to retain all the information and avoid viewing any individual item as noise, we allow clusters to be formed from single items as well. This leads us to have 58 individual clusters. The more items there are in a cluster, the more alike they will be in terms of their popularity as determined by the community. This means that the individual items carry a greater amount of novelty compared with the rest. If we omit the clusters with many items, we obtain a slightly more interactive diagram, which is also easier to look at.
Each item is a link to the Dribbble page dedicated to that particular shot, so you can view it yourself. Including thumbnails of the shots here is not possible for legal reasons. The result is somewhat hard to interpret—for instance, the circle on the right corresponds to an item which has received quite a lot of views, but which doesn't necessarily rank best in the number of comments or hearts received. The item at the top of the diagram is also the first item on the Dribble list. The leftmost item has received a disproportionate number of comments relative to its views and hearts. Each item has its own specifics, so if you are interested, you can learn more about them. This diagram shows that there are plenty of great examples to explore. It is not given to lead to the false conclusion that only these examples are worth exploring. These are just examples we have some preliminary information about.
Exploring Codepen data
Codepen is a similar place, but directed more towards web designers and developers, who can publish their work and get feedback on it. As on Dribbble, there is a lot of creative work waiting to be explored. However, what is sometimes forgotten is that the website doesn't just show an end result, but also pushes people to share the code that led to it, so that everyone could create a similar version (or enhance an existing one) by themselves. A very positive effect I noticed while preparing this post was that the designers on both sites seem to influence each other or recreate their work by novel means which were different from the original author's intention. For instance, an animation of variable-width lines along the path of the mathematical sign for infinity was first created and animated on Dribbble before someone else found a way to update its style on Codepen.
Codepen doesn't seem to show unique content on every page—sometimes the same demos appeared under different pages. There were some pages which showed only four demos at most. With 12 demos/page, we would expect the first 1000 pages to have 12000 demos, but the sample contained only 11224 unique links.
Applying DBSCAN on this dataset turned out to be impossible. We run out of memory, no matter how we choose to tune the parameters. For this data size, it would be more appropriate to use k-means, so we pick 20 initial clusters. The following is the result of the k-means clustering after applying a 2D PCA decomposition on the original data.
We see again that there is a lot of overlap and some clusters contain a very large number of items, which aren't even visible. Once we see how many items each cluster has and think in terms of how many demos a visitor of this page would be willing to visit, we may choose to omit clusters containing more than 25 demos. This is how we obtain the following diagram, where all the items are clickable again.
Here we can see, depending on the color, in which cluster each item falls. This is how we could explore interesting new content that we haven't seen before. And we don't have to view 11224 different demos to see some great examples. But again, we should expect that some great new demos won't be visible here.
Both lists are only temporary and likely to change over time. But I hope that this post has given you an overview how you could seek interesting designs without spending too much time on it.