Abstract
BackgroundFrequent expression of negative emotion words on social media has been linked to depression. However, metrics have relied on average values, not dynamic measures of emotional volatility.ObjectiveThe aim of this study was to report on the associations between depression severity and the variability (time-unstructured) and instability (time-structured) in emotion word expression on Facebook and Twitter across status updates.MethodsStatus updates and depression severity ratings of 29 Facebook users and 49 Twitter users were collected through the app MoodPrism. The average proportion of positive and negative emotion words used, within-person variability, and instability were computed.ResultsNegative emotion word instability was a significant predictor of greater depression severity on Facebook (rs(29)=.44, P=.02, 95% CI 0.09-0.69), even after controlling for the average proportion of negative emotion words used (partial rs(26)=.51, P=.006) and within-person variability (partial rs(26)=.49, P=.009). A different pattern emerged on Twitter where greater negative emotion word variability indicated lower depression severity (rs(49)=−.34, P=.01, 95% CI −0.58 to 0.09). Differences between Facebook and Twitter users in their emotion word patterns and psychological characteristics were also explored.ConclusionsThe findings suggest that negative emotion word instability may be a simple yet sensitive measure of time-structured variability, useful when screening for depression through social media, though its usefulness may depend on the social media platform.
Highlights
Extending How Social Media Language Predicts Depression “With as much as we have learned about emotions, it is as if we have been taking still photos of a dance” [1].Social media is used in different ways by different people, but for many individuals, status updates provide snapshots of their lived experience
Mann-Whitney U tests revealed no significant differences between Facebook and Twitter groups in the length of recording period sampled (U=590.50, P=.22), though there were differences in the median time difference (U=344.00, P
The findings suggest that instability in the negative emotion content across Facebook status updates may be a useful indicator for depression and that the time-adjusted mean squared successive difference (MSSD) is an effective index of instability that accounts for the uneven temporal sampling of social media posts
Summary
Extending How Social Media Language Predicts Depression “With as much as we have learned about emotions, it is as if we have been taking still photos of a dance” [1].Social media is used in different ways by different people, but for many individuals, status updates provide snapshots of their lived experience. Studies to date have primarily considered how the relative frequency of words indicating positive and negative emotion relate to other characteristics such as mental health status, or which words (or set of words) best predict different outcomes. Such studies indicate that the frequent expression of negative emotion words in status updates can accurately identify individuals experiencing symptoms of depression [2,3,4,5,6]. An individual’s mental health is reflected by more than just the average frequency or the type of words used; variability in emotional expression over time might provide http://www.jmir.org/2018/5/e168/ XSLFO RenderX. Metrics have relied on average values, not dynamic measures of emotional volatility
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
