The paper entitled "Information Cascades in Feed-based Networks of Users with Limited Attention," by S. Sreenivasan, K. Chan, A. Swami, G. Korniss, and B. Szymanski was published by the IEEE Trans. Network Science and Eng., 4(2):120-128, see link below.

The paper entitled "Information Cascades in Feed-based Networks of Users with Limited Attention," by Sameet Sreenivasan, Kevin S. Chan, Ananthram Swami, Gyorgy Korniss, and Boleslaw Szymanski was published by the IEEE Transactions on Network Science and Engineering, 4(2):120-128. The paper focuses on 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. The authors study the impact of the extent of user attention on the probability that the cascade becomes viral. The authors demonstrate that beyond a certain attention span, cascades tend to become viral. The critical forwarding probabilities have an inverse relationship with the attention span. Finally, the paper analyzes an event-specific dataset obtained from Twitter, and show that estimated branching factors correlate well with the cascade-size distributions associated with distinct hashtags. The paper is available at the link above.