• Не использовали понятие ретвита — «reposting more inclusive than “retweeting”»
  • Анализ распространения исключительно по времени и фолловингу.

• «however, we find that predictions of which particular user or URL will generate large cascades are relatively unreliable»
• «diffusion can only be harnessed reliably by targeting large numbers of potential influencers, thereby capturing average effects»
• «marketing strategies, defined by the relative cost of identifying versus compensating potential «influencers»

Не каскад и не нейронная сеть с трешхолдом:

These three assignments effectively make different assumptions about the influence process:

• “first influence” rewards primacy, assuming that individuals are influenced when they

first see a new piece of information, even if they fail to immediately act on it, during which time they may see it again;

• “last influence” assumes the opposite, instead attributing influence to the most recent exposure; and “split influence”

assumes either that the likelihood of noticing a new piece of information,

• or equivalently the inclination to act on it,

accumulates steadily as the information is posted by more

To identify consistently influential individuals, we aggre- gated all URL posts by user and computed individual-level influence as the logarithm of the average size of all cascades for which that user was a seed. We then fit a regression tree model [6], in which a greedy optimization process recur- sively partitions the feature space, resulting in a piecewise- constant function where the value in each partition is fit to the mean of the corresponding training data.

Although content was not found to improve predictive per- formance, it remains the case that individual-level attributes— in particular past local influence and number of followers— can be used to predict average future influence. Given this observation, a natural next question is how a hypothetical marketer might exploit available information to optimize the di ff usion of information by systematically targeting certain classes of individuals.

To illustrate this point we now evaluate the cost-e ff ectiveness of a hypothetical targeting strategy based on a simple but plausible family of cost functions c i = c a +f i c f , where c a rep- resents a fixed “acquisition cost” c a per individual i, and c f represents a “cost per follower” that each individual charges the marketer for each “sponsored” tweet. Without loss of generality we have assumed a value of c f = $0.01, where the choice of units is based on recent news reports of paid tweets (http://nyti.ms/atfmzx). For convenience we express the acquisition cost as multiplier α of the per-follower cost;

http://dealbook.nytimes.com/2009/11/23/a-friends-tweet-could-be-an-ad/ hence c a = α c f .