Loughborough University
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What about mood swings? Identifying depression on Twitter with temporal measures of emotions

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conference contribution
posted on 2019-03-18, 09:50 authored by Xuetong Chen, Martin SykoraMartin Sykora, Thomas Jackson, Suzanne ElayanSuzanne Elayan
Depression is among the most commonly diagnosed mental disorders around the world. With the increasing popularity of online social network platforms and the advances in data science, more research efforts have been spent on understanding mental disorders through social media by analysing linguistic style, sentiment, online social networks and other activity traces. However, the role of basic emotions and their changes over time, have not yet been fully explored in extant work. In this paper, we proposed a novel approach for identifying users with or at risk of depression by incorporating measures of eight basic emotions as features from Twitter posts over time, including a temporal analysis of these features. The results showed that emotion-related expressions can reveal insights of individuals’ psychological states and emotions measured from such expressions show predictive power of identifying depression on Twitter. We also demonstrated that the changes in an individual’s emotions as measured over time bear additional information and can further improve the effectiveness of emotions as features, hence, improve the performance of our proposed model in this task.



  • Business and Economics


  • Business

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WWW 2018 Conference - The Sixth International Workshop on Natural Language Processing for Social Media (SocialNLP 2018)


CHEN, X. ... et al., 2019. What about mood swings? Identifying depression on Twitter with temporal measures of emotions. IN: Companion Proceedings of the The Web Conference 2018 (WWW 2018), Lyon, France, April 23 - 27, pp. 1653-1660


© 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License


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This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

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This is an Open Access Conference Paper. It is published by IW3C2 under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/




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Lyon, France