Slashdot Mirror
Online Social Networks Can Be Tipped By Less Than 1% of Their Population
An anonymous reader writes "A new algorithm developed by researchers at West Point seems to break new ground for viral marketing practices in online social networks. Assuming a trend or behavior that spreads in an online social network based on the classic 'tipping' model from sociology (based on the work of Thomas Schelling and Mark Granovetter), the new West Point algorithm can find a set of individuals in the network that can initiate a social cascade – a progressive series of 'tipping' incidents — which leads to everyone in the social network adopting the new behavior. The good news for viral marketers is that this set of individuals is often very small – a sample of the Friendster social network can be influenced when only 0.8% of the initial population is seeded. The trick is finding the seed set. The algorithm is described in a paper to be presented later this summer at the prestigious IEEE ASONAM conference."
But which 1%, There are a lot of 1% out there.
The Tea Party is controlled by the Oil Companies 1%.
The Occupy is controlled by the Unions 1%
The Favorite Trend is controlled by the Marketers 1%...
It doesn't seem that you have any decisions to make for yourself, There is always someone else telling you what to think.
If something is so important that you feel the need to post it on the internet... It probably isn't that important.
"The trick is finding the seed set." No, it's not. The real trick is finding the seed set of the seed set. On Facebook, you have 900 million users. 1% of that is 9 million, which is too large to influence. But 1% of that 1% is just 90,000, something that a targeted advertising campaign might be able to influence.
Any chance they're just witnessing C&C nodes transmitting spam orders or pagerank gaming links to the remaining 99.2% of Friendster accounts (all of which are hacked and forgotten)?
It's a comp sci paper that is looking for connected nodes in a network, and they're using copies of data sets of social networks as their starting point. They aren't monitoring networks looking for "who is exerting influence over them", they're looking for nodes that are well connected to other nodes, presuming those represent the most valuable people to convince.
Now, could those "friends and families" in the network data actually be there as part of a botnet controller and its zombie minions? Sure, why not? But each one of those would be a single node in the set of nodes as having the right connections. Doesn't mean that marketing to the botherder or the botnet is going to get you much business, but if you were looking for someone who has influence, it would identify the botherder and not the bots themselves.
John