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Personality homophily and the local network characteristics of Facebook

Noe, Nyala, Whitaker, Roger and Allen, Stuart 2016. Personality homophily and the local network characteristics of Facebook. Presented at: The 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, San Francisco, CA, USA, 18 - 21 August 2016. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, pp. 386-393. 10.1109/ASONAM.2016.7752263

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Abstract

Social networks are known to form on the basis of homophily, where nodes with some type of similar characteristics are more likely to be connected. Some of the most fundamental human characteristics are reflected by an individual's personality, which represents a persistent disposition governing a human's outlook and approach to diverse situations. While taking into account demographics of age and gender, we assess the extent to which personality homophily is evident in the local network features of Facebook. Using a large sample obtained from the MyPersonality dataset, we find that a range of network-based features correlate with personality facets of individuals. In particular, extraversion had a positive effect on an individual's network size, while neuroticism had a negative effect. Additionally, extraversion and openness were positively related to transitivity, which was moderated by gender. Finally, we found that conscientiousness, agreeableness and extraversion were homophilous: people with higher similarity on these facets were more strongly connected. This was additionally mediated by gender for agreeableness: personality similarity had an effect for male-only and mixed pairs, but not for female-only pairs. Personality similarity was also stronger among closed triangles, compared to open ones. These results support the idea that inherent attraction between individuals, on the basis of personality, drives the roles we play within our online social networks.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: IEEE
ISBN: 978-1-5090-2846-7
Date of First Compliant Deposit: 1 September 2016
Last Modified: 07 Aug 2019 14:16
URI: http://orca.cf.ac.uk/id/eprint/94124

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