Information Cascades in Feed-based Networks of Users with Limited Attention

Abstract

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.

8 pages, 5 figures, For IEEE Transactions on Network Science and Engineering (submitted)

References in corpus (4)

• Sandpile on Scale-Free Networks
• Competing for Attention in Social Media under Information Overload Conditions
• Predicting Successful Memes using Network and Community Structure
• Anticipating Activity in Social Media Spikes

Cited by in corpus (6)

• Universality, criticality and complexity of information propagation in social media
• Branching process descriptions of information cascades on Twitter
• Social Networks through the Prism of Cognition
• The Paradox of Information Access: Growing Isolation in the Age of Sharing
• Infectivity Enhances Prediction of Viral Cascades in Twitter
• Smart Information Spreading for Opinion Maximization in Social Networks