Making social networking software relevant: A Napkin Plan
Charlie over at This is going to be BIG! has very smart readers, one of whom–Gabe Morris–points out the flaws in existing social networking software. I’ve got some great ideas on how to fix this stuff, too.
How to Build a Social Network:
LinkedIn annoys people to the extent that it connects you without relevance. The basis for LinkedIn and Friendster’s automatic relevance is degrees of separation. But this has weaknesses – there are second degree contacts who I have very little in common with, while I am sure there are hundreds of people in the sixth degree and beyond that I would have plenty in common with.
Right now on Friendster I’ve got maybe 100 friends–the product of a drunken summer ‘03–very few of whom have anything in common. Some are just people I added not to be rude, some are bosom buddies, and some are acquaintances. Neither Friendster nor any other social networking software, to my knowledge, takes this into consideration. Instead, it lumps them all into the category “My Friends,” and recommends random selections from this hodge-podge of people to others as people they should get to know. How helpful. To solve this, a social networking site needs to provide people with the tools to quickly and easily describe their social networks. Easiest way to to this? Tags!
Here’s what I’m thinking. Each user gets to describe their contacts using tags, preferably tags which describe that contact’s relation to them. To seed this process, the software could have a set of recommended defaults: co-worker, ex, drinking-buddy, boss, annoying, douchbag, crush, meh, etc., etc. These tags need to be private, because otherwise it’s a public opinion, which limits the usefulness of the data. How many people want their boss to know that they tagged him with both “boss” and “douchebag?” You only get to see your own tags.
This would provide a better dataset to evaluate relevance: the software would recommend contacts which share the same tags as you. If you’re a bicycle nut, you get potential riding buddies; if you’re into radical feminism, you get hooked up with other femsexies; if you’re a douchebag, it’ll hook you up with all the other jerks. This shouldn’t be a deterministic process, however, otherwise it would limit recommendations to particular cliques. Weighting is essential, and I’m sure there’s some maths post-doc all full of coffee with a few ideas about how to tease further correlations out of this dataset.
So who wants to do this?
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