Inferring mechanisms of response prioritization on social media under information overload

Abstract

Human decision-making is subject to the biological limits of cognition. The fluidity of information propagation over online social media often leads users to experience information overload. This in turn affects which information received by users are processed and gain a response to, imposing constraints on volumes of, and participation in, information cascades. In this study, we investigate properties contributing to the visibility of online social media notifications by highly active users experiencing information overload via cross-platform social influence. We analyze simulations of a coupled agent-based model of information overload and the multi-action cascade model of conversation with evolutionary model discovery. Evolutionary model discovery automates mechanistic inference on agent-based models by enabling random forest importance analysis on genetically programmed agent-based model rules. The mechanisms of information overload have shown to contribute to a multitude of global properties of online information cascades. We investigate nine characteristics of online messages that may contribute to the prioritization of messages for response. Our results indicate that recency had the largest contribution to message visibility, with individuals prioritizing more recent notifications. Global popularity of the conversation originator had the second highest contribution, and reduced message visibility. Messages that presented opportunity for novel user interaction, yet high reciprocity showed to have relatively moderate contribution to message visibility. Finally, insights from the evolutionary model discovery results helped inform response prioritization rules, which improved the robustness and accuracy of the model of information overload.

Can Social Norms Explain Long-Term Trends in Alcohol Use? Insights from Inverse Generative Social Science

“…These studies are also notable in using data mining on candidate models arising from multiple runs of the iGSS process in order to identify commonalities in the components of the agents' utility function. Gunaratne and colleagues' evolutionary model discovery framework has also been applied to understanding the mechanisms of message prioritisation by social media users, using empirical data from social media platforms (Gunaratne et al 2021).…”

Generating Mixed Patterns of Residential Segregation: An Evolutionary Approach

“…In particular, we hypothesize 6 causal factors, including racial bias, that might influence residential satisfaction and the household's selection of a neighborhood for relocation, and in turn, result in the emergence of mixed patterns: mean neighborhood age, distance from current residence, isolation, tendency to relocate, mean residential satisfaction of neighbors, and racial similarity. We then explore the ability to generate mixed patterns when including these factors and their interactions in Hatna and Benenson's recent extension of Schelling's original model (Hatna & Benenson 2015) using evolutionary model discovery (Gunaratne et al 2021;Gunaratne & Garibay 2020;Gunaratne 2019). Evolutionary model discovery works in two stages: 1) evolving models that better represent the macro-phenomenon of interest (mixed patterns) through the genetic programming of agent rules, and 2) random forest factor importance analysis on the data generated by the genetic program to identify factors that contribute highly to models that better represent the desired macro-phenomenon.…”

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“…This mechanism is generally called attention. Viewer attention when watching sport through television may take place in two ways (Breuer & Rumpf, 2015;Gross, 2014;Gunaratne et al, 2021). For example, when a football match is watched, a viewer's top-down attention is most probably focused on the match itself, including the football pitch, players, ball moves, goals, scores, exciting scenes, etc.…”

“…This mechanism is generally called attention. Viewer attention when watching sport through television may take place in two ways (Breuer and Rumpf, 2015; Gross, 2014; Gunaratne et al. , 2021).…”