RSDD-time : temporal annotation of self-reported mental health diagnoses
- Author
- Sean MacAvaney, Bart Desmet (UGent) , Arman Cohan, Luca Soldaini, Andrew Yates, Ayah Zirikly and Nazli Goharian
- Organization
- 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.
- Keywords
- LT3
Downloads
-
(...).pdf
- full text
- |
- UGent only
- |
- |
- 577.82 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8573300
- MLA
- MacAvaney, Sean, et al. “RSDD-Time : Temporal Annotation of Self-Reported Mental Health Diagnoses.” Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, Association for Computational Linguistics, 2018, pp. 168–73.
- APA
- MacAvaney, S., Desmet, B., Cohan, A., Soldaini, L., Yates, A., Zirikly, A., & Goharian, N. (2018). RSDD-time : temporal annotation of self-reported mental health diagnoses. Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, 168–173. New Orleans, USA: Association for Computational Linguistics.
- Chicago author-date
- MacAvaney, Sean, Bart Desmet, Arman Cohan, Luca Soldaini, Andrew Yates, Ayah Zirikly, and Nazli Goharian. 2018. “RSDD-Time : Temporal Annotation of Self-Reported Mental Health Diagnoses.” In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, 168–73. New Orleans, USA: Association for Computational Linguistics.
- Chicago author-date (all authors)
- MacAvaney, Sean, Bart Desmet, Arman Cohan, Luca Soldaini, Andrew Yates, Ayah Zirikly, and Nazli Goharian. 2018. “RSDD-Time : Temporal Annotation of Self-Reported Mental Health Diagnoses.” In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, 168–173. New Orleans, USA: Association for Computational Linguistics.
- Vancouver
- 1.MacAvaney S, Desmet B, Cohan A, Soldaini L, Yates A, Zirikly A, et al. RSDD-time : temporal annotation of self-reported mental health diagnoses. In: Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic. New Orleans, USA: Association for Computational Linguistics; 2018. p. 168–73.
- IEEE
- [1]S. MacAvaney et al., “RSDD-time : temporal annotation of self-reported mental health diagnoses,” in Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, New Orleans, USA, 2018, pp. 168–173.
@inproceedings{8573300,
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.}},
author = {{MacAvaney, Sean and Desmet, Bart and Cohan, Arman and Soldaini, Luca and Yates, Andrew and Zirikly, Ayah and Goharian, Nazli}},
booktitle = {{Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic}},
keywords = {{LT3}},
language = {{eng}},
location = {{New Orleans, USA}},
pages = {{168--173}},
publisher = {{Association for Computational Linguistics}},
title = {{RSDD-time : temporal annotation of self-reported mental health diagnoses}},
url = {{http://aclweb.org/anthology/W18-0618}},
year = {{2018}},
}