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      Opinion Formation on the Internet: The Influence of Personality, Network Structure, and Content on Sharing Messages Online

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          Abstract

          Today the majority of people uses online social networks not only to stay in contact with friends, but also to find information about relevant topics, or to spread information. While a lot of research has been conducted into opinion formation, only little is known about which factors influence whether a user of online social networks disseminates information or not. To answer this question, we created an agent-based model and simulated message spreading in social networks using a latent-process model. In our model, we varied four different content types, six different network types, and we varied between a model that includes a personality model for its agents and one that did not. We found that the network type has only a weak influence on the distribution of content, whereas the message type has a clear influence on how many users receive a message. Using a personality model helped achieved more realistic outcomes.

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          Most cited references77

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          Collective dynamics of 'small-world' networks.

          Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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            The Strength of Weak Ties

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              The spread of behavior in an online social network experiment.

              How do social networks affect the spread of behavior? A popular hypothesis states that networks with many clustered ties and a high degree of separation will be less effective for behavioral diffusion than networks in which locally redundant ties are rewired to provide shortcuts across the social space. A competing hypothesis argues that when behaviors require social reinforcement, a network with more clustering may be more advantageous, even if the network as a whole has a larger diameter. I investigated the effects of network structure on diffusion by studying the spread of health behavior through artificially structured online communities. Individual adoption was much more likely when participants received social reinforcement from multiple neighbors in the social network. The behavior spread farther and faster across clustered-lattice networks than across corresponding random networks.
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                Author and article information

                Contributors
                Journal
                Front Artif Intell
                Front Artif Intell
                Front. Artif. Intell.
                Frontiers in Artificial Intelligence
                Frontiers Media S.A.
                2624-8212
                02 July 2020
                2020
                : 3
                : 45
                Affiliations
                Human Computer Interaction Center, RWTH Aachen University , Aachen, Germany
                Author notes

                Edited by: Panagiotis Germanakos, SAP SE, Germany

                Reviewed by: Yilun Shang, Northumbria University, United Kingdom; Mattia Zanella, University of Pavia, Italy

                *Correspondence: Laura Burbach burbach@ 123456comm.rwth-aachen.de

                This article was submitted to AI for Human Learning and Behavior Change, a section of the journal Frontiers in Artificial Intelligence

                Article
                10.3389/frai.2020.00045
                7861255
                33733162
                0a0bd2c1-98aa-4f90-aefa-cb28149c2c5f
                Copyright © 2020 Burbach, Halbach, Ziefle and Calero Valdez.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 31 October 2019
                : 25 May 2020
                Page count
                Figures: 5, Tables: 1, Equations: 0, References: 81, Pages: 16, Words: 12620
                Categories
                Artificial Intelligence
                Original Research

                opinion formation,personality traits,message spread,social networks,network types,latent process model

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