Abstract
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.
Highlights
Instant access to vast quantities of information has resulted in online social media users experiencing the effects of information overload (Gomez-Rodriguez et al 2014; Hodas et al 2013; Koroleva et al 2010; Li et al 2014; Feng et al 2015)
We introduce a mechanistic model of information exchange over an asynchronous communication medium, both informed by results from analytical studies on online social media activity (Gomez-Rodriguez et al 2014; Roetzel 2019) and motivated by concepts from extended-self theory (Belk 2014) and working memory (Cowan 2001)
The resulting information overload affects the overall dynamics of conversations and information propagation
Summary
Instant access to vast quantities of information has resulted in online social media users experiencing the effects of information overload (Gomez-Rodriguez et al 2014; Hodas et al 2013; Koroleva et al 2010; Li et al 2014; Feng et al 2015). We introduce a mechanistic model of information exchange over an asynchronous communication medium, both informed by results from analytical studies on online social media activity (Gomez-Rodriguez et al 2014; Roetzel 2019) and motivated by concepts from extended-self theory (Belk 2014) and working memory (Cowan 2001). With this model we analyze the effects of information overload on population-scale conversation properties and information dynamics.
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