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Your Digital News Reading Habits Reflect Your Personality

Constantinides, M; Germanakos, P; Samaras, G; Dowell, J; (2018) Your Digital News Reading Habits Reflect Your Personality. In: (Proceedings) 26th Conference on User Modeling, Adaptation and Personalization. (pp. pp. 45-48). Association for Computing Machinery (ACM): New York, USA. Green open access

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Abstract

The way people read digital news - as distinct from what news they read - has emerged as a significant concern for research in user modelling and personalisation. Intuitively, some people read the news frequently and broadly whilst others read it occasionally and selectively. It is likely that these differences in news reading behaviour arise in part from differences in peoples' personalities. We report a study that surveyed the digital news reading habits and personality traits of 241 people. We find correlations between most news reading characteristics (e.g., how much time over a day a person reads news) and some personality traits (e.g Openness-to-Experience). The correlations provide a better understanding of the different types of news reading user and why they read news in different ways. They indicate the value of extending user model profiles to include personality traits along with domain specific activity factors.

Type: Proceedings paper
Title: Your Digital News Reading Habits Reflect Your Personality
Event: 26th Conference on User Modeling, Adaptation and Personalization
Location: Singapore
Dates: 08 July 2018 - 11 July 2018
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3213586.3226191
Publisher version: https://doi.org/10.1145/3213586.3226191
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10052657
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