  The inhibiting effects of information overload on the behavior of online
social media users, can affect the population-level characteristics of
information dissemination through online conversations. We introduce a
mechanistic, agent-based model of information overload and investigate the
effects of information overload threshold and rate of information loss on
observed online phenomena. We find that conversation volume and participation
are lowest under high information overload thresholds and mid-range rates of
information loss. Calibrating the model to user responsiveness data on Twitter,
we replicate and explain several observed phenomena: 1) Responsiveness is
sensitive to information overload threshold at high rates of information loss;
2) Information overload threshold and rate of information loss are
Pareto-optimal and users may experience overload at inflows exceeding 30
notifications per hour; 3) Local abundance of small cascades of modest global
popularity and local scarcity of larger cascades of high global popularity
explains why overloaded users receive, but do not respond to large, highly
popular cascades; 4) Users typically work with 7 notifications per hour; 5)
Over-exposure to information can suppress the likelihood of response by
overloading users, contrary to analogies to biologically-inspired viral spread.
Reconceptualizing information spread with the mechanisms of information
overload creates a richer representation of online conversation dynamics,
enabling a deeper understanding of how (dis)information is transmitted over
social media.
