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Social Networks Science

Online Social Networks Can Be Tipped By Less Than 1% of Their Population 125

Posted by samzenpus
from the you-are-all-individuals dept.
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."
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Online Social Networks Can Be Tipped By Less Than 1% of Their Population

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  • by eldavojohn (898314) * <[moc.liamg] [ta] [nhojovadle]> on Monday June 04, 2012 @01:54PM (#40211109) Journal

    a sample of the Friendster social network can be influenced when only 0.8% of the initial population is seeded.

    Friendster? Wow, you could influence, like, 300 people!

    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)?

  • by Anonymous Coward on Monday June 04, 2012 @02:04PM (#40211229)

    Does this remind anyone of Locke and Demosthenes from Ender's Game? Seeding a few carefully worded articles to change the discourse of the network?

  • by timeOday (582209) on Monday June 04, 2012 @02:08PM (#40211269)
    "The trick is finding the seed set." No, you still have to influence the seed set, which might be really hard.

    Let's say this model predicts that I can end terrorism by converting 100 radical muslims to buddhism. How does that help me? (Simply sending in drones to remove these nodes from the graph, so to speak, will not have the same effect).

    Second example, let's say my novel is almost guaranteed to be successful if it gets a glowing review in the New York Times. Well, how hard can that be? Usually trusted nodes are trusted for some reason - because they're reliable. That means they're hard to influence.

  • by Baloroth (2370816) on Monday June 04, 2012 @02:27PM (#40211521)

    That's not really what this statistic is saying. It is using a "tipping" model, where it assumes anyone who has more than 50% of his friends exhibiting the behavior will automatically adopt it. A useful model, but not actually true (like nearly all mathematical models, it is only approximately true in the real world). That means they only have to find a "seed" population to adopt the trend: the model says if all of them adopt it, everyone on the network will. Think of it less like sheep and more like dominoes: you only need to trigger one dominoe to trigger the rest, but that presumes a carefully constructed ideal system. In reality, 99% of people may be sheep, but this study says absolutely nothing about that. It assumes it, rather than proving it.

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