The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Depression and Self-Harm Risk Assessment in Online Forums
Open accessAbstract
Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a neural framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset ("RSDD") consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.
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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
(2014) · Cited by 33,419
Bag of Tricks for Efficient Text Classification
Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers · doi:10.18653/v1/e17-2068 · Cited by 4,545
A Convolutional Neural Network for Modelling Sentences
Kalchbrenner, N., Grefenstette, E., & Blunsom, P. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1062 · Cited by 3,601
Natural language processing with Python
Bird, S., Klein, E., & Loper, E. (2009) · CERN Document Server (European Organization for Nuclear Research) · Cited by 3,468
Kiros, R., Zhu, Y., Salakhutdinov, R., Zemel, R. S., Torralba, A., Urtasun, R., & Fidler, S. (2015) · arXiv · doi:10.48550/arxiv.1506.06726 · Cited by 1,293
Mental Health Discourse on reddit: Self-Disclosure, Social Support, and Anonymity
De Choudhury, M., & De, S. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14526 · Cited by 836
Quantifying Mental Health Signals in Twitter
Coppersmith, G., Dredze, M., & Harman, C. (2014) · Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality · doi:10.3115/v1/w14-3207 · Cited by 722
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media
De Choudhury, M., Kiciman, E., Dredze, M., Coppersmith, G., & Kumar, M. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858207 · Cited by 683
Ferrari, A. J., Norman, R. E., Freedman, G., Baxter, A. J., Pirkis, J. E., Harris, M. G., Page, A., Carnahan, E., Degenhardt, L., Vos, T., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0091936 · Cited by 480
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Coppersmith, G., Leary, R., Crutchley, P., & Fine, A. (2018) · Biomedical Informatics Insights · doi:10.1177/1178222618792860 · Cited by 506
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Zhang, T., Schoene, A. M., Ji, S., & Ananiadou, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00589-7 · Cited by 476
Low, D. M., Rumker, L., Talkar, T., Torous, J., Cecchi, G., & Ghosh, S. S. (2020) · Journal of Medical Internet Research · doi:10.2196/22635 · Cited by 340
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
Ji, S., Pan, S., Li, X., Cambria, E., Long, G., & Huang, Z. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2020.3021467 · Cited by 257
Metadata
- Authors
- Yates, A., Cohan, A., & Goharian, N.
- Venue
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Published
- 2017
- Pages
- 2968-2978
- OpenAlex
- W2753465613
- Cited by
- 323
- References
- 33
- FWCI
- 32.18
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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