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

TL;DR

This paper models information cascades in feed-based networks considering users' limited attention, analyzing how attention span influences viral spread, and validates findings with simulations and Twitter data.

Contribution

It introduces a semi-analytical model linking attention span and cascade virality, validated through simulations and real-world Twitter data analysis.

Findings

Critical forwarding probabilities decrease as attention span increases.

Beyond a certain attention span, cascades can become viral at specific forwarding thresholds.

Analytical branching factors align well with simulation results and Twitter data.

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.