scholarly journals An Agent-Based Model of Urgent Diffusion in Social Media

2015 ◽  
Vol 18 (2) ◽  
Author(s):  
William Rand ◽  
Jeffrey Herrmann ◽  
Brandon Schein ◽  
Neža Vodopivec
Keyword(s):  
Social Media ◽  
Agent Based Model ◽  
Agent Based

scholarly journals Agent Based Model of Anti-Vaccination Movements: Simulations and Comparison with Empirical Data

Vaccines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 809
Author(s):  
Pawel Sobkowicz ◽  
Antoni Sobkowicz
Keyword(s):  
Social Media ◽  
Critical Parameters ◽  
Rational Approach ◽  
The Internet ◽  
High Temporal Resolution ◽  
Nonlinear Feedback ◽  
Agent Based Model ◽  
Linear Processes ◽  
Agent Based ◽  
The Social

Background: A realistic description of the social processes leading to the increasing reluctance to various forms of vaccination is a very challenging task. This is due to the complexity of the psychological and social mechanisms determining the positioning of individuals and groups against vaccination and associated activities. Understanding the role played by social media and the Internet in the current spread of the anti-vaccination (AV) movement is of crucial importance. Methods: We present novel, long-term Big Data analyses of Internet activity connected with the AV movement for such different societies as the US and Poland. The datasets we analyzed cover multiyear periods preceding the COVID-19 pandemic, documenting the behavior of vaccine related Internet activity with high temporal resolution. To understand the empirical observations, in particular the mechanism driving the peaks of AV activity, we propose an Agent Based Model (ABM) of the AV movement. The model includes the interplay between multiple driving factors: contacts with medical practitioners and public vaccination campaigns, interpersonal communication, and the influence of the infosphere (social networks, WEB pages, user comments, etc.). The model takes into account the difference between the rational approach of the pro-vaccination information providers and the largely emotional appeal of anti-vaccination propaganda. Results: The datasets studied show the presence of short-lived, high intensity activity peaks, much higher than the low activity background. The peaks are seemingly random in size and time separation. Such behavior strongly suggests a nonlinear nature for the social interactions driving the AV movement instead of the slow, gradual growth typical of linear processes. The ABM simulations reproduce the observed temporal behavior of the AV interest very closely. For a range of parameters, the simulations result in a relatively small fraction of people refusing vaccination, but a slight change in critical parameters (such as willingness to post anti-vaccination information) may lead to a catastrophic breakdown of vaccination support in the model society, due to nonlinear feedback effects. The model allows the effectiveness of strategies combating the anti-vaccination movement to be studied. An increase in intensity of standard pro-vaccination communications by government agencies and medical personnel is found to have little effect. On the other hand, focused campaigns using the Internet and social media and copying the highly emotional and narrative-focused format used by the anti-vaccination activists can diminish the AV influence. Similar effects result from censoring and taking down anti-vaccination communications by social media platforms. The benefit of such tactics might, however, be offset by their social cost, for example, the increased polarization and potential to exploit it for political goals, or increased ‘persecution’ and ‘martyrdom’ tropes.

scholarly journals An Agent-Based Model of Urgent Diffusion in Social Media

2013 ◽  
Author(s):  
Jeffrey Herrmann ◽  
William M. Rand ◽  
Brandon Schein ◽  
Neza Vodopivec
Keyword(s):  
Social Media ◽  
Agent Based Model ◽  
Agent Based

scholarly journals Inferring mechanisms of response prioritization on social media under information overload

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chathika Gunaratne ◽  
William Rand ◽  
Ivan Garibay
Keyword(s):  
Social Media ◽  
Information Overload ◽  
User Interaction ◽  
Evolutionary Model ◽  
Online Information ◽  
Information Cascades ◽  
Agent Based Model ◽  
Agent Based ◽  
Highly Active ◽  
Online Social Media

AbstractHuman 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.

scholarly journals Opinion Expression Dynamics in Social Media Chat Groups: An Integrated Quasi-Experimental and Agent-Based Model Approach

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Siyuan Ma ◽  
Hongzhong Zhang
Keyword(s):  
Social Media ◽  
Peer Support ◽  
Online Communication ◽  
Opinion Dynamics ◽  
Agent Based Model ◽  
Agent Based ◽  
Individual Level ◽  
Opinion Expression ◽  
Quasi Experimental ◽  
The Individual

Social media chat groups, such as WeChat and WhatsApp groups, are widely applied in online communication. This research has conducted two studies to examine the individual level and collective level’s opinion dynamics in those groups. The opinion dynamic is driven by two variables, people’s perceived peer support and willingness of opinion expression. The perceived peer support influences the willingness of opinion expression, and the willingness influences the dynamics of real opinion-expression. First, the quasi-experimental study recruited twenty-five participants as the observation group and found that decreasing perceived peer support would significantly increase individuals’ expression willingness to protect his/her opinion. To generalize the individual level findings to a collective level, the second study treated the social media chat groups as an undirected fully-connected social network and simulated people’s opinion expression dynamics with an agent-based model. The simulation indicated that (1) with the help of increased willingness of opinion expression, the minority opinion supporters as a collective did not fall silent but continue to express themselves and (2) increasing willingness of opinion expression would maintain the existence of minority opinion but could not help the minority reverse to the majority.

Sign in / Sign up

Export Citation Format

Share Document