RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses

Publication type
U
Publication status
Published
Authors
MacAvaney, S., Desmet, B., Cohan, A., Soldaini, L., Yates, A., Zirikly, A., & Goharian, N.
Series
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic
Pagination
168-173
Publisher
Association for Computational Linguistics (New Orleans, USA)
Conference
Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic (CLPsych 2018) (New Orleans, USA)
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Abstract

Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the temporality of mental health diagnoses. In this work, we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Furthermore, we include exact temporal spans that relate to the date of diagnosis. This information is valuable for various computational methods to examine mental health through social media because one’s mental health state is not static. We also test several baseline classification and extraction approaches, which suggest that extracting temporal information from self-reported diagnosis statements is challenging.