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OSN Mood Tracking: Exploring the Use of Online Social Network Activity as an Indicator of Mood Changes

Lee, James Alexander, Efstratiou, Christos, Bai, Lu (2016) OSN Mood Tracking: Exploring the Use of Online Social Network Activity as an Indicator of Mood Changes. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. . pp. 1171-1179. ISBN 978-1-4503-4462-3. (doi:10.1145/2968219.2968304)

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https://doi.org/10.1145/2968219.2968304

Abstract

Online social networks (OSNs) have become an integral part of our everyday lives, where we share our thoughts and feelings. This study analyses the extent to which the changes of an individual’s real-world psychological mood can be inferred by tracking their online activity on Facebook and Twitter. By capturing activities from the OSNs and ground truth data via experience sampling, it was found that mood changes can be detected within a window of 7 days for 61% of the participants by using specific, combined online activity signals. The participants fall into three distinct groups: those whose mood correlates positively with their online activity, those who correlate negatively and those who display a weak correlation. We trained two classifiers to identify these groups using features from their online activity, which achieved precision of 95.2% and 84.4% respectively. Our results suggest that real-world mood changes can be passively tracked through online activity on OSNs.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1145/2968219.2968304
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.9.H85 Human computer interaction
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Depositing User: Christos Efstratiou
Date Deposited: 04 Jul 2016 09:38 UTC
Last Modified: 29 May 2019 17:33 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/56207 (The current URI for this page, for reference purposes)
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