Comment by lifeisstillgood | Hacker News Reader

Comment by lifeisstillgood | Hacker News Reader

lifeisstillgood

This is a pretty clever hack. The problem is if the tipping point theory is correct (#) (that you will perform behaviour X if a sufficent number of your immediate circle also are performing behaviour X) you still need to find a subset of the network to start the ball rolling.

The solution is simple - say the tipping point is 20 friends must do X for you to do it. So walk the whole network and find everyone with more than 20 friends. Remove those with the most friends (say the those > 99th percentile).

Now walk the network again, and find those with more than 20 friends and again 99th percentile goes. Eventually you remove the 20th friend from everyone and those left have 19 or fewer friends.

Now tell all these seed group to do behaviour X.

Now put back the very last set you removed. And they are guaranteed to have at least 20 friends, and all those friends will be doing behaviour X. Now put back the penultimate group, and because the most recent arrivals are now also doing behaviour X ....

Problems:

1. For any social network at a given point there is just one seed group. Right now (or 6 days ago) this group is being identified. And sold.

2. the tipping point theory as a whole is a bit dodgy (see below).

3. Feasibility - they only mentioned orders of magnitude smaller subsets. Lets be kind and suggest that its 3 orders of magnitude. for LinkedIn that leaves a seed group of 100,000's. Not the size you can just invite into a focus group.

Overall, really a cool hack, and I swear its worth ponying up on and selling to excited digital agencies

(#) Good article on Debunking of tipping point theory - http://www.fastcompany.com/641124/tipping-point-toast

Edit: Wanted to rewrite my below comment that got a bit confused

In terms of #3, it's even worse than 100,000's - for LinkedIn it would likely be millions:

"In general, online social networks had the smallest seed sets - 13 networks of this type had an average seed set size less than 2% of the population (these networks were all in Category A). We also noticed, that for most networks, there was a linear realtion between threshold value and seed size"

Though, for a company with direct access to their users via the UI, it would be a fairly trivial task to reach a significant subset. LinkedIn could reasonably push UI updates only to the target population. Given they have full access in the first place, I'm uncertain as to why they would want to engage in this form of marketing, though.

>> I'm uncertain as to why they would want to engage in this form of marketing, though

They might not want to on their own, however if some advertiser really had the desire to try to hit the entire seed population then LinkedIn could sell that target population at a higher CPM because of the relatively high projected value of those individuals.

> 2. the tipping point theory as a whole is a bit dodgy (see below). > (#) Good article on Debunking of tipping point theory - http://www.fastcompany.com/641124/tipping-point-toast

It seems to me that the article doesn't debunk at all the existence of a tipping point in social networks phenomenons.

In fact, it clearly states that it is the law of the fews, "that rare, highly connected people shape the world", that seem to be inexistent in Watts experiments.

If trends are really like forest fires, then there is a tipping point ; it is just not required to have these highly connected people on board to reach it.

It makes an interesting case for Facebook's paid messaging service. Combined with some well crafted / personalized messaging, you could probably get a large number to click through. Even if the seed group is a million users, at a dollar per user that's only a million dollars. Large marketers spend that much on advertising without even blinking. If it gave them a reasonable chance at going viral, no doubt many would consider it well worth the investment. The only problem would be the potential backlash when people noticed that it had started in such an artificial way.

a rewrite after reading the paper got lost in my noprocrast setting (no fault of server I just gave up).

anyway they use % base not absolute threshold (obviously really) but there are some lovely big questions coming out of this - if anyone is at PyCon UK this weekend and interested in throwing some thoughts around please shout.

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