And You Will Know Us by the Company We Keep — Remains of the Day
The problem of graph design:
When designing an app that shapes its user experience off of a social graph, how do you ensure the user ends up with the optimal graph to get the most value out of your product/service?
The fundamental attribution error has always been one of my cautionary mental models. The social media version of is over-attributing how people behave on a social app to their innate nature and under-attributing it to the social context the app places them in.
(...) what if there was a way to build an interest graph for you without you having to follow anyone? What if you could skip the long and painstaking intermediate step of assembling a social graph and just jump directly to the interest graph? And what if that could be done really quickly and cheaply at scale, across millions of users? And what if the algorithm that pulled this off could also adjust to your evolving tastes in near real-time, without you having to actively tune it?
The problem with approximating an interest graph with a social graph is that social graphs have negative network effects that kick in at scale. Take a social network like Twitter: the one-way follow graph structure is well-suited to interest graph construction, but the problem is that you’re rarely interested in everything from any single person you follow.
(...) I noted that social networks tend to compete on three axes: social capital, entertainment, and utility. Focusing just on entertainment, the problem with building a content feed off of a person’s social graph is that, to be blunt, we don’t always find the people we know to be that entertaining. I love my friends and family. That doesn’t mean I want to see them dancing the nae nae. Or vice versa. Who we follow has a disproportionate effect on the relevance and quality of what we see on much of Western social media because the apps were designed that way.