US Military Scientists Solve a Fundamental Problem of Viral Marketing
technologyreview.com
technologyreview.com
This is illustrative of what bothers me about the paper: they're already trying to market the algorithm, and they're not being honest about its limitations. Now I'm uncertain of whether to trust them on the network analysis part at all.
Take the cited example: What is the return? It will vary wildly and take into account factors like how related the message is to the product/service, previous exposure, quality and likelihood to share, and website quality/conversion rate. Second, what is the investment? Marketing costs can range from 0 to many tens or even hundreds of thousands.
To those who say the "message is all that matters": relevant, quality content fails to go viral all the time. It's easy to think it doesn't happen if you don't work in marketing, since you'll never see it. Identifying key people effectively really can have value in a marketing campaign. That said, connected people (also most people) will likely ignore you if your message or content is shit (uninteresting, unsurprising, unclear, etc.)
1) Quality/On-Topic
2) Correctly Timed with current events
3) Luck
It's actually quite similar to startups in general when you think about it.
For instance this WV commercial
http://www.youtube.com/watch?v=HnL-7x4n4d8
would never have been possible to do by WV themselves. Yet it is exactly this kind of commercials that have a high chance of going viral.
With regard to the 297 figure, my guess is that it had a context that didn't make it into the article. His papers are pretty well reasoned and quite upfront about the capabilities of a given process or algorithm or strategy, so I find it unlikely that he's gone all used-car salesman this time around.
*Picking nits: the author of the article misspelled Shakarian's name.
If you want to propagate a meme through a population... you get the picture.
When everyone got computers that were faster than calculators, hedge funds and bank trading desks bought mainframes and colocated them next to or with the exchanges' servers and all of the trading volume moved to high-frequency trading-friendly dark pools and the exchanges...
In the context of political influence, what does everyone think all of the campaign money goes toward these days? A lot of angry people start revolutions and there is nothing technology can do to placate someone(s)who is being abused for any reason and certainly not at scale!
All of these marketing firms are chasing the mass market consumers/voters that have had diminishing spending power for over a decade now. Here's a tip: build something really expensive and desirable for someone really rich because they have lots of spending power and few things to do with the money.
Still, dubbing even this ROI would be a huge stretch.
What they did figure out "slick approach to a well-known problem [in graph theory]" then sell it as something that viral marketers could actually use. I see no evidence that this graph theoretic problem, although it was motivated by various real world problems, is actually a computational problem that people try to solve in the real world.
In fact, I'm pretty sure viral marketers would simply target products at people with lots of facebook friends who pass some kind of "not fake" test.
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 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.
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.
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.
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.
Consider a future that is beyond the present in which social network analysis is used for identification, targeting, and disruption of social networks of all kinds ...something like OWS if you will...; when the subject type of research is implemented to understand how to prevent opposition by those who's interest it is that you and those around you don't oppose, cannot organize, and are disrupted faster by knowing exactly who the linchpin is that has to be neutralized to disperse any organization.
"...Solve the Fundamental Problem of Viral Marketing" Nothing is solved. This is just another tool to use for a while.
If it was so easy to get something viral, then a blanket message sent to a large group of people would automatically result in viral marketing. ie - spam. And we see how often spam becomes viral....
Well, the message matters and the initial set of people also matters.
A good message has a higher probability to be transfered by a peer to its friends. So yeah spam emails don't go viral because they have a very low probability to be transfered. But given two messages of the same quality, they may or may not go viral depending on where you inject them.
To clarify, the actual scientific paper do not claim that they "solved the fundamental problem of viral marketing" they just propose a new heuristic to find good seeds and show that it's good.
Edit: clarity.
"A Scalable Heuristic for Viral Marketing Under the Tipping Model"
(That's actually been true for a long time. The exploits are only getting better now. It's a bit like SSL.)
Obviously, being able to identify high value "seeds" is paramount, but it appears to be more related to cost-reduction (not having to contact more seeds than necessary).
Centrality measures, along with propagation simulation algorithms, already helped identifying seeds...but without "the proper content", I doubt that good seed classification can, alone, "solve the problem".
Reading this, it appears that finding seed sets is an old problem. Normally people focus on finding minimum-size seed sets, but here they're just focusing on small ones. However, they don't appear to have actually proven any upper bounds on the sizes of the seed sets found this way; they've just observed that empirically it's small. Which is still useful.
I suppose a similar result is reached by sorting users in a sub-graph by the time they spend online on that social network, since more time spent online probably means more "friends".
Troll for 10 hours and not interact...0 new friends. Interact for 1 hour in a quality way...2 new friends.
It seems to me that in the real world, this group would be people with the most friends, or people with very diverse friends, kind of a no-brainer.
From the article they remove those who have the most connections first. Lets say that if more than 10 of your LinkedIn links have photos, then you will upload one too. So they find everyone who has >10 friends, order them and remove the top 20% of most connected individuals. Then repeat. Stop when you have a group of people none of whom has >10 (extant) friends.
This group of people can then be given a virus (behaviour/whatever). Now put back the most recently removed group of people. It is guaranteed that each of those new people will all have 10+ friends all of whom exhibit this new virus.
Its pretty clever. Now I need to grab graph-tool and start playing !
Also:
> Lastly, we find that highly clustered local neighborhoods, together with dense network-wide community structures, suppress a trend's ability to spread under the tipping model.
That matters and frankly is the future of the internet. We shall most likely see geo-physical mesh neighborhoods. Always on, mobile or not, connectivity to the people around you. It probably will make a resurgance of democracy and community, likely to solve enourmous caching problems, and utterly destroy loads of business models. And yes ! its Maths and Science that proves its !
Also, wouldn't many of the same people be members of the seed group, leading to message fatigue amongst their connections?
Seems like something out of which you may raise the hopes of many a marketing department, but which ultimately proves impracticable.
The question I had was how to find the tipping point. Is this done through tests on a smaller group?
It would be like my saying, "25% is sufficient", and in a later sentence saying, "eight out of thirty-two is enough". They both make the same statement.