有限注意用户反馈网络中的信息级联
Information Cascades in Feed-based Networks of Users with Limited Attention
摘要：
我们建立了一个模型的信息级联反馈为基础的网络，考虑到有限的注意广度的用户，消息生成率和消息转发率。使用这个模型，我们通过模拟研究，用户注意力的程度上的级联成为病毒的概率的影响。在类比与分支过程中，我们估计的分支因子与级联过程中不同的注意广度和不同的转发概率，并证明，超过一定的注意广度，关键的转发概率存在，构成一个阈值后，级联可以成为病毒。关键转发概率与注意广度成反比关系。接下来，我们为我们的模型开发了一种半分析方法，该方法允许我们确定给定消息生成率，消息转发率和注意广度值的分支因子。使用这种分析方法得到的分支因子显示出良好的协议，通过模拟得到的。最后，我们分析了从Twitter获得的事件特定数据集，并表明估计的分支因子与不同标签相关的级联大小分布相关。
英文摘要：
We build a model of information cascades on feed-based networks, taking into account the finite attention span of users, message generation rates and message forwarding rates. Using this model, we study through simulations, the effect of the extent of user attention on the probability that the cascade becomes viral. In analogy with a branching process, we estimate the branching factor associated with the cascade process for different attention spans and different forwarding probabilities, and demonstrate that beyond a certain attention span, critical forwarding probabilities exist that constitute a threshold after which cascades can become viral. The critical forwarding probabilities have an inverse relationship with the attention span. Next, we develop a semi-analytical approach for our model, that allows us determine the branching factor for given values of message generation rates, message forwarding rates and attention spans. The branching factors obtained using this analytical approach show good agreement with those obtained through simulations. Finally, we analyze an event specific dataset obtained from Twitter, and show that estimated branching factors correlate well with the cascade size distributions associated with distinct hashtags.
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