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Cognitive reflection correlates with behavior on Twitter.

ABSTRACT: We investigate the relationship between individual differences in cognitive reflection and behavior on the social media platform Twitter, using a convenience sample of N?=?1,901 individuals from Prolific. We find that people who score higher on the Cognitive Reflection Test-a widely used measure of reflective thinking-were more discerning in their social media use, as evidenced by the types and number of accounts followed, and by the reliability of the news sources they shared. Furthermore, a network analysis indicates that the phenomenon of echo chambers, in which discourse is more likely with like-minded others, is not limited to politics: people who scored lower in cognitive reflection tended to follow a set of accounts which are avoided by people who scored higher in cognitive reflection. Our results help to illuminate the drivers of behavior on social media platforms and challenge intuitionist notions that reflective thinking is unimportant for everyday judgment and decision-making.

SUBMITTER: Mosleh M

PROVIDER: S-EPMC7875970 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Cognitive reflection correlates with behavior on Twitter.

Mosleh Mohsen M Pennycook Gordon G Arechar Antonio A AA Rand David G DG

Nature communications 20210210 1

We investigate the relationship between individual differences in cognitive reflection and behavior on the social media platform Twitter, using a convenience sample of N = 1,901 individuals from Prolific. We find that people who score higher on the Cognitive Reflection Test-a widely used measure of reflective thinking-were more discerning in their social media use, as evidenced by the types and number of accounts followed, and by the reliability of the news sources they shared. Furthermore, a ne ...[more]

PMID: 33568667

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Project description:BackgroundTwitter represents a social media platform through which medical cannabis dispensaries can rapidly promote and advertise a multitude of retail products. Yet, to date, no studies have systematically evaluated Twitter behavior among dispensaries and how these behaviors influence the formation of social networks.ObjectivesThis study sought to characterize common cyberbehaviors and shared follower networks among dispensaries operating in two large cannabis markets in California.MethodsFrom a targeted sample of 119 dispensaries in the San Francisco Bay Area and Greater Los Angeles, we collected metadata from the dispensary accounts using the Twitter API. For each city, we characterized the network structure of dispensaries based upon shared followers, then empirically derived communities with the Louvain modularity algorithm. Principal components factor analysis was employed to reduce 12 Twitter measures into a more parsimonious set of cyberbehavioral dimensions. Finally, quadratic discriminant analysis was implemented to verify the ability of the extracted dimensions to classify dispensaries into their derived communities.ResultsThe modularity algorithm yielded three communities in each city with distinct network structures. The principal components factor analysis reduced the 12 cyberbehaviors into five dimensions that encompassed account age, posting frequency, referencing, hyperlinks, and user engagement among the dispensary accounts. In the quadratic discriminant analysis, the dimensions correctly classified 75% (46/61) of the communities in the San Francisco Bay Area and 71% (41/58) in Greater Los Angeles.ConclusionsThe most centralized and strongly connected dispensaries in both cities had newer accounts, higher daily activity, more frequent user engagement, and increased usage of embedded media, keywords, and hyperlinks. Measures derived from both network structure and cyberbehavioral dimensions can serve as key contextual indicators for the online surveillance of cannabis dispensaries and consumer markets over time.

Dataset Information

Cognitive reflection correlates with behavior on Twitter.

Publications

Cognitive reflection correlates with behavior on Twitter.

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