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Do online voting patterns reflect evolved features of human cognition? An exploratory empirical investigation

Do online voting patterns reflect evolved features of human cognition? An exploratory empirical investigation
Do online voting patterns reflect evolved features of human cognition? An exploratory empirical investigation
Online votes or ratings can assist internet users in evaluating the credibility and appeal of the information which they encounter. For example, aggregator websites such as Reddit allow users to up-vote submitted content to make it more prominent, and down-vote content to make it less prominent. Here we argue that decisions over what to up- or down-vote may be guided by evolved features of human cognition. We predict that internet users should be more likely to up-vote content that others have also up-voted (social influence), content that has been submitted by particularly liked or respected users (model-based bias), content that constitutes evolutionarily salient or relevant information (content bias), and content that follows group norms and, in particular, prosocial norms. 489 respondents from the online social voting community Reddit rated the extent to which they felt different traits influenced their voting. Statistical analyses confirmed that norm-following and prosociality, as well as various content biases such as emotional content and originality, were rated as important motivators of voting. Social influence had a smaller effect than expected, while attitudes towards the submitter had little effect. This exploratory empirical investigation suggests that online voting communities can provide an important test-bed for evolutionary theories of human social information use, and that evolved features of human cognition may guide online behaviour just as it guides behaviour in the offline world.
1932-6203
e0129703-[15pp]
Priestley, Maria
e1ee95f7-4430-4d06-8964-7afc6333d87f
Mesoudi, Alex
be831ad6-6096-4085-843a-b1ae4d47bc58
Priestley, Maria
e1ee95f7-4430-4d06-8964-7afc6333d87f
Mesoudi, Alex
be831ad6-6096-4085-843a-b1ae4d47bc58

Priestley, Maria and Mesoudi, Alex (2015) Do online voting patterns reflect evolved features of human cognition? An exploratory empirical investigation. PLoS ONE, 10 (6), e0129703-[15pp]. (doi:10.1371/journal.pone.0129703).

Record type: Article

Abstract

Online votes or ratings can assist internet users in evaluating the credibility and appeal of the information which they encounter. For example, aggregator websites such as Reddit allow users to up-vote submitted content to make it more prominent, and down-vote content to make it less prominent. Here we argue that decisions over what to up- or down-vote may be guided by evolved features of human cognition. We predict that internet users should be more likely to up-vote content that others have also up-voted (social influence), content that has been submitted by particularly liked or respected users (model-based bias), content that constitutes evolutionarily salient or relevant information (content bias), and content that follows group norms and, in particular, prosocial norms. 489 respondents from the online social voting community Reddit rated the extent to which they felt different traits influenced their voting. Statistical analyses confirmed that norm-following and prosociality, as well as various content biases such as emotional content and originality, were rated as important motivators of voting. Social influence had a smaller effect than expected, while attitudes towards the submitter had little effect. This exploratory empirical investigation suggests that online voting communities can provide an important test-bed for evolutionary theories of human social information use, and that evolved features of human cognition may guide online behaviour just as it guides behaviour in the offline world.

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Accepted/In Press date: 12 May 2015
Published date: 11 June 2015
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 384265
URI: https://eprints.soton.ac.uk/id/eprint/384265
ISSN: 1932-6203
PURE UUID: bc2bb809-b0fe-47a8-a743-da913fc979f9

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Date deposited: 21 Dec 2015 11:27
Last modified: 20 Dec 2017 17:31

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