Summary: Study uncovers how misinformation and fake news can spread via social media platforms like Twitter. Those with high numbers of mutual followers are more likely to spread “dreadful” misinformation. Findings could offer solutions to prevent fake news dissemination.
Source: Osaka University
In an Internet-driven world, social media has become the go-to source of all kinds of information. This is especially relevant in crisis-like situations, when warnings and risk-related information are actively circulated on social media. But currently, there is no way of determining the accuracy of the information. This has occasionally resulted in the spread of misinformation, with some readers often bearing the brunt. In a study published in Japanese Psychological Research, scientists at Osaka University, including Prof Asako Miura, found a pattern through which information spreads on social media –which could help prevent the spread of fake news. Prof Miura says, “Dissemination of information through social media is often associated with false rumors. In order to prevent this, we wanted to unravel the underlying mechanisms by digging deeper into how these false rumors spread.”
The scientists focused on Twitter, a popular site where users can disseminate or share information through the “retweet” feature. Conventional models of information diffusion fail to adequately explain the exact transmission route on social media, as they do not take into account individual user characteristics. Therefore, to study these characteristics, the scientists first selected 10 highly retweeted (more than 50 times) risk-related tweets. Based on Slovic’s well-known definition of risk perception, a cognitive model used to assess how people perceive certain risks, they assessed whether users perceived these risks as “dreadful” (related to large-scale events with potentially dire consequences) or “unknown” (when the impact of the event is unknown). They then analyzed the personal networks of the users who tweeted/retweeted particular tweets –specifically the number of followers, followees, and mutual connections.
They found that users with fewer connections tend to spread information arbitrarily, possibly owing to a lack of experience or awareness. But, users with a high number of mutual connections were more emotionally driven –they were more likely to spread dreadful information, possibly intending to share their reactions with the public. Prof Miura explains, “Our study showed the existence of an information diffusion mechanism on social media that cannot be explained by conventional theoretical models. We showed that risk perception has a significant impact on the ‘retweetability’ of tweets.”
By identifying the user network characteristics on Twitter, this study potentially offers a solution to prevent fake news dissemination. These characteristics can be leveraged to maximize the spread of accurate information, ensuring that appropriate measures are taken. Prof Miura concludes, “Our research provides an opportunity for people to rethink how false information is spread and to deliver accurate information via social media.”
NeuroscienceNews would like to thank Saori Obayashi for submitting this article for inclusion on the website.
Source:
Osaka University
Media Contacts:
Saori Obayashi – Osaka University
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The image is in the public domain.
Original Research: Open access
“Spread of Risk Information Through Microblogs: Twitter Users with More Mutual Connections Relay News That is More Dreadful”. Masashi Komori, Asako Miura, Naohiro Matsumura, Kai Hiraishi, Kazutoshi Maeda.
Japanese Psychological Research doi:10.1111/jpr.12272.
Abstract
Spread of Risk Information Through Microblogs: Twitter Users with More Mutual Connections Relay News That is More Dreadful
In elucidating the spread of risk information through microblogging, it is important to understand the behaviors of numerous average users, in addition to the activities of authorities. We followed the transmission pathways of 10 actual widely spread tweets concerning several risk information topics, including natural disasters, nuclear disasters, and infectious diseases, and we identified the types of risk that affected retweeting by classifying each tweet based on Slovic’s risk‐perception model. Furthermore, we examined the types of users who did and did not retweet the information. Users with few connections in the form of followers (i.e., people who are following a user) or followees (people a user is following), or with a low ratio of mutual followers within their connections, had a tendency to retweet a large amount of risk information, regardless of the type of risk involved. On the other hand, users with a high ratio of mutual followers exhibited a greater tendency to retweet risk information when it was perceived as dreadful, though they did not retweet risk information much on the whole. These results suggest that there are two mechanisms by which risk information is spread within the Twitter network: information exchange and social sharing of personal reactions.
