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.
Highlights
Social networks such as Facebook, Instagram and Twitter are integrated into most people’s everyday lives
We further introduce the latent process model, on which we built our simulation and explain further aspects that are important for the agent-based simulation
After considering the influence of the content type, we look at the different network types and how they influence the number of forwarding agents
Summary
Social networks such as Facebook, Instagram and Twitter are integrated into most people’s everyday lives. While searching for information and integrating it into their opinion formation, users are no longer just passive recipients of information in online social networks, but are actively spreading their own opinions (Hóllig and Hasebrink, 2016; Li et al, 2017; Frees and Koch, 2018). Social networks play a powerful role and influence the formation of opinion of individuals, but can play a decisive role in political situations and decisions (Guille et al, 2013). It has been shown, that social networks have a strong influence on political decisions. One example for this was the American presidential election in 2008, where many people perceived a strong influence of Twitter on the elections (Hughes and Palen, 2009; Shang, 2019)
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