OSN mood tracking: Exploring the use of online social network activity as an indicator of mood changes

James Alexander Lee, Lu Bai, Christos Efstratiou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationUbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PublisherAssociation for Computing Machinery, Inc
Pages1171-1179
Number of pages9
ISBN (Electronic)9781450344623
DOIs
Publication statusPublished - 12 Sep 2016
Event2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, Germany
Duration: 12 Sep 201616 Sep 2016

Conference

Conference2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
CountryGermany
CityHeidelberg
Period12/09/1616/09/16

Keywords

  • Emotion
  • Facebook
  • Mood
  • Online Social Networks
  • Sentiment Analysis.
  • Social Psychology
  • Twitter

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