In this post, I’m excited to present a framework for classifying social media platforms.
Aside from helping to illustrate how and why successful apps have earned a place in our lives, I hope it may also serve as a “playbook” for new entrants who hope to carve out their own places in the landscape.
The main concepts are represented in the graphic below, and the rest of this essay provides insight into my development process.
To read about how I used this framework to derive generalizations about different types of social communities, check out my companion piece: Insights from the Unified Theory of Social.
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Table of Contents
- A Utility-Based Approach
- Dependence on User Cooperation
- Dependence on Personal Relationships
- The Completed Framework
- Complications, Caveats, etc.
- In Conclusion
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A Utility-Based Approach
For any product to be successful, it must provide a utility to its users by either solving a problem or fulfilling a desire. Put another way, a product’s utility answers the question “Why do users come back?”
I’m presently building a new consumer social app, and as we iterate toward product-market fit, I found myself contemplating this question more deeply.
To that end, I compiled the following list of user motivations:
Why do users return to social media?
- To be entertained
- To interact with friends
- To express oneself
- To be validated
- To connect with others
- To stay informed
- To gain knowledge
- To buy things
- To make money
For the above list, I tried to reduce every idea down to its most basic expression. For example, motivations like “To grow a following” and “To be influential” were omitted in lieu of more deep-rooted desires like being validated, expressing oneself, or making money. In other words, what were the core reasons why a user would even care about being an influencer?
Once I felt reasonably confident in the list, I started trying to draw similarities and differences between the items. I find this to be a super valuable practice because it often reveals some deeper conclusions hiding beneath the surface.
One early categorization looked like the following:
| To consume | To feel good | To achieve goals |
| To stay informed To gain knowledge To buy things | To be entertained To interact with friends To be validated | To express oneself To connect with others To make money |
While the categories on this first attempt felt “correct”, they didn’t seem especially “useful”. Statements like “users return to social media to feel good” or “to achieve goals” didn’t really tell us anything new or interesting.
I continued brainstorming, and was eventually struck by an observation.
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Dependence on User Cooperation
Despite developing the list of user motivations for “social” media, I was surprised by how many items didn’t depend on actual social interaction. For example, being entertained or staying informed could be very solitary experiences. On the other hand, some motivations like “being validated” required by definition another user (to do the “validating”).
So there was clearly a line to be drawn between which motivations required user “cooperation”1, and which did not.
This line of thinking resulted in the following, far more interesting categorization of reasons why users return to social media:
| User motivations requiring LOW Cooperation | User motivations requiring HIGH Cooperation |
| To be entertained To stay informed To gain knowledge To buy things To express oneself | To interact with friends To connect with others To be validated To make money To express oneself |
Indeed, every social media platform involves varying degrees of cooperation with others. For example, satisfaction on Snapchat is almost entirely dependent upon interactions between users (which are even plotted out using emoji hieroglyphics), but on the other hand, enjoyment of TikTok relies very little on cooperation with other users. I merely swipe up to continue watching whatever I want to watch, in private.
This distinction felt pretty useful, and to test the value of this conceptual model, I attempted to evaluate popular social media platforms based on their “cooperation” factor. Put another way, to what extent is the value derived by users dependent upon interactions with other users?
Answering this question resulted in the following diagram:
To me, it was a good sign that platforms fell across the entire continuum. That meant that “user cooperation” was meaningful as a way to characterize different communities.
As a further observation, I noted that “low cooperation” platforms (on the left side) tend to rely on high-quality content to drive continued engagement. For example, users go to Youtube and Soundcloud to watch great videos and listen to great music. The fact that they can interact with other users in the comments sections is incidental to their enjoyment of the content. So it seemed to make sense to call these platforms “content-driven”.
Conversely, on ”high cooperation” platforms (the right side), the content itself can take an almost secondary role to the interactions between users. For example, users might not remember all the photos they sent or received on Snap or all the topics discussed on Clubhouse, but they’ll remember the relationships they built through interacting on the platforms. I chose to call these platforms “interaction-driven”.
Takeaway: Social platforms can be either content-driven or interaction-driven in terms of how they provide value to users.
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Dependence on Personal Relationships
Ok, so we’d established one interesting measure for evaluating social platforms. I was inspired at this point to develop another measure, which would enable us to plot a “2 by 2” matrix for visually representing the social media landscape.
So I kept on digging.
My next insight was fueled by a This American Life podcast, in which teenage girls were interviewed about how they used Instagram. In a previous write-up, I’d observed the extreme extent to which the girls’ personal relationships drove their interactions on Instagram.
On the other end of the spectrum, I spend a lot of time on Reddit where user anonymity is almost a feature of the community. It’s no surprise, then, that personal relationships have little to no bearing on user interactions on Reddit.2
The fact that Instagram and Reddit were so different in this way – despite ranking similarly on “user cooperation” above – made it feel as though we’d stumbled upon a potential secondary axis for our analysis.
The comparison made sense in the bigger picture, too.
If a primary goal of social media was building human connection, platforms could achieve it by either 1) sparking new connections, or 2) deepening existing ones. Every platform would employ some combination of the two, and we could evaluate the extent to which they relied on each.
A literal application of this concept might focus on whether user relationships were built on the platform itself or transposed from elsewhere.3 Or – insofar as the best measure of human connection on a platform is the interaction behavior between users, we could instead ask: To what extent is user behavior dependent upon real-life relationships?
Answering this question resulted in the following diagram:
Like the previous diagram, this one also showed a great distribution of platforms across the continuum, and I was glad to see that Reddit was not the only platform to meaningfully change its position.4
As a further observation I noticed that platforms on the left side, which relied less on personal relationships, were often marked by more sophisticated discovery mechanisms. This makes sense, because in a world where users don’t have social contacts to direct their interactions, a platform must work harder to present opportunities for users to consume and connect (think Reddit r/all or the Youtube homepage).
On the other hand, platforms on the right side, which rely more heavily on personal relationships, tend to reinforce those existing connections. This causes experiences to feel more insular or “clique-ish”. Take Facebook and LinkedIn for example, whose “home” experiences are comprised of status updates from your existing connections.
Takeaway: Social platforms can be either discovery-oriented or clique-oriented in terms of how users connect with others and consume content.
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The Completed Framework
Since we’d successfully established two axes of measurement, it was time we put them together in a 2 by 2. Combining the two charts we’d already created resulted in the following graphic:
I was very pleased to see that the four quadrants formed natural groupings of social media platforms; so natural, in fact, that it seemed proper to give each quadrant a name and description.
From there, I was able to generate the following generalized graphic for illustrating the landscape of social media platforms (and the four main types of communities).
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Complications, Caveats, etc.
Admittedly, it’s hard for a single graphic to properly characterize all of the complex interactions found on social platforms. Here I present areas where the model encounters challenges.
The “Creator” Class of Users
Many social platforms – especially content-driven communities like YouTube, Soundcloud, etc. – feature a distinct “creator” class of user, who are responsible for producing the majority of content published on a platform. This is because content-driven communities rely on great content to keep users coming back, and unfortunately not all users have the interest, time, or talent to succeed as creators.
This distinction complicates our model because the “creator” experience is often non-trivially different from that of the average “consumer”. Creators often have access to different tools and UIs, and the way in which they interact with other creators and consumers can be completely different.
For example, a Soundcloud musician may derive “value” from receiving likes and follows from listeners. Based on our measure of “user cooperation” we might want to call Soundcloud more “interaction-driven”. But to that extent, every platform provides ways for consumers to rate content. If we considered interactions between consumers and creators, then every platform might potentially be classified as “interaction-driven”.
To address this complication, I decided to only consider interactions between “consumers”.5
For the User Cooperation measure, we only considered interactions between “consumer” users.
It’s a subtle distinction, but very impactful.
In the case of Soundcloud, consumer-to-consumer interactions are generally limited to seeing what other users are liking and following, and maybe exchanging comments on a track. As a result, it ranks lower on the cooperation dependency, but still higher than other platforms where you can’t see what other users are viewing (like YouTube).
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In Conclusion
So there we have it, a framework for classifying social media platforms as a function of their dependence on either user cooperation or personal relationships.
It’s all well and good to sort the various social platforms into boxes, but I think the framework really gets useful when we use it to start making generalizations and drawing conclusions about each type of platform. For more on that, check out my companion piece: Insights from The Unified Theory of Social.
Finally, I’d like to emphasize that this framework is a work in progress6 and it’s entirely possible (and probable) that I’ve gotten some things wrong. If you think I missed a platform or classified anything incorrectly, I’m interested in hearing feedback! Let’s talk about it in the comments below or find me on any “interaction-driven” platform.
Footnotes:
- I’ve defined “cooperation” as the extent to which interaction from other users drives the successful achievement of a goal.
- In fact, despite frequently sharing posts with each other over SMS, my partner and I have had zero interaction on Reddit and don’t even know each others’ usernames.
- As an aside, one of my favorite examples of “relationship transposition” powered by a product feature was Snap’s “scan QR code to follow” (“Snapcodes”). It suited the app perfectly because users were primarily following people that they knew in real life. So when you were physically with someone that you wanted to follow, they would just pull up their QR code from their app, and you would point your camera at their phone to find their profile. This was so much slicker than having to type in a username, and it worked precisely because people were “snapping” with friends in their existing social circle.
- For example, Clubhouse was very dependent on user interaction (chart #1), but much less dependent upon personal relationships (chart #2). This is reflected in both the core interaction on Clubhouse (live, interactive discussions) as well as the exploration mechanic, which encourages dropping in on rooms based on your personal interests, rather than just hanging with friends. Another big mover, but in the opposite manner, was Substack. It ranked low in user interaction, but quite a bit higher on personal relationships. This is because users tend to come to Substack primarily to read articles rather than interact with other readers, and the articles they read are usually written by authors who they discovered via either personal or professional connections (rather than the platform itself).
- Despite calling them “classes”, it’s possible for users to be both a consumer and a creator depending on how they’re using a platform at any specific moment. The classification is based on behavior rather than membership.
- Not the least because platforms tend to evolve and change over time!
