People may be unwilling to sort themselves into different networks as they organize their online news feeds. Credit is given to Egan Jimenez, a student at the University.
The study found that people may be unwilling to sort themselves into different networks as they browse their online news feeds.
The team developed a model of complex contagions, which is used to study how behavior spreads in groups, instead of applying it to how reaction to news coverage may spread online. They tested their theoretical model with data from the micro-blogging site.
When people are less interested in news, their online environment is politically mixed. Users who constantly share and react to their preferred news sources are more likely to foster a politically isolated network.
Users miss out on more news articles when they are in the bubbles. Users seem to avoid what they deem to be unimportant news at the expense of missing out on subjectively important news, according to the model.
The researchers conclude that the high rates of American political divisiveness and social distrust could be due to all of this.
"Our study shows that, even without social media, coverage from biased news outlets is changing users' social connections and pushing them into so-called political 'echo chambers' where they are surrounded by others who share their same political identity and beliefs," said Christopher Tokita PhD. Whether a user ignores or reacts to certain news posts can help determine if their social network will become more diverse.
Working with Andy Guess, assistant professor of politics and public affairs at the Princeton School of Public and International Affairs, and Corina Tarnita, professor of ecology and evolutionary biology with the Princeton Department of Ecology and Evolutionary Biology, Tokita studied these behaviors by building a theoretical model and testing its predictions
The idea of "information cascades" was central to their modeling and was the process of individuals observing and mimicking the actions of others so that a wide online shift occurs. The collective behavior seen in schools of fish or insect swarms is similar to this phenomenon.
They show that the sharing of viral news stories can lead people to conclude that some of the friends they follow on social media are misrepresenting the news as reported by their own preferred outlets. Users unintentionally sort themselves into divided networks when they "unfollow" untrustworthy connections.
The model was tested with data from the four news outlets and found that they had 1,000 followers each. They used the complete follower network of users to record who followed and who didn't in the summer of 2020 to track political ideology and shifting social networks.
They found online trends and behaviors that may contribute to political polarization. The follower demographic of CBS News and U.S. Today was more conservative than the ones of the Washington Examiner and Vox. Users who follow CBS News and U.S. Today tend to have more political and ideological diversity than those who follow the Washington Examiner.
Online interactions can't account for the divisive shift in American politics, but they have influenced human behavior and relationships. The study shows that knowledge of political ideology is not necessary for social networks to be politically divided for users.
It's not hard to find evidence of differing opinions on social media, but we don't know how social media can drive people apart. Our contribution is to show how people organize their feeds on online social networks. Guess said that this can occur even without knowing other users' partisan identities.
The research team wants to know how these trends may contribute to the spread and consumption of "fake news" and misinformation, and how inaccurate news fuels political division among the public. The study suggests that people who consume and share fake news might be isolating themselves from everyone else who follows mainstream sources. This should be explored further.
Our results show the power of a cross-disciplinary approach to the study of political polarization, despite being derived from a simple theoretical model of collective dynamics. Tarnita said they hope that they may inspire future exams into social network-specific algorithms and patterns as potential contributors to societal polarization.
The paper, "Polarized information ecosystems can reorganize social networks via information cascades," will be published in December.
The National Academy of Sciences has a paper on more information: Polarized information ecosystems can reorganize social networks. There is a DOI of 10.1073/pnas.
The National Academy of Sciences has a journal.
People unintentionally group themselves together online, fueling political polarization across the US.
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