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Download fileWhat about mood swings? Identifying depression on Twitter with temporal measures of emotions
conference contribution
posted on 2019-03-18, 09:50 authored by Xuetong Chen, Martin SykoraMartin Sykora, Thomas Jackson, Suzanne ElayanSuzanne ElayanDepression 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.
History
School
- Business and Economics
Department
- Business