The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Detection of Depression-Related Posts in Reddit Social Media Forum
Open accessAbstract
Depression is viewed as the largest contributor to global disability and a major reason for suicide. It has an impact on the language usage reflected in the written text. The key objective of our study is to examine Reddit users' posts to detect any factors that may reveal the depression attitudes of relevant online users. For such purpose, we employ the Natural Language Processing (NLP) techniques and machine learning approaches to train the data and evaluate the efficiency of our proposed method. We identify a lexicon of terms that are more common among depressed accounts. The results show that our proposed method can significantly improve performance accuracy. The best single feature is bigram with the Support Vector Machine (SVM) classifier to detect depression with 80% accuracy and 0.80 F1 scores. The strength and effectiveness of the combined features (LIWC+LDA+bigram) are most successfully demonstrated with the Multilayer Perceptron (MLP) classifier resulting in the top performance for depression detection reaching 91% accuracy and 0.93 F1 scores. According to our study, better performance improvement can be achieved by proper feature selections and their multiple feature combinations.
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Cites
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Depression Is the Leading Cause of Disability Around the World
Friedrich, M. J. (2017) · JAMA · doi:10.1001/jama.2017.3826 · Cited by 1,459
Pyszczynski, T., & Greenberg, J. (1987) · Psychological Bulletin · doi:10.1037/0033-2909.102.1.122 · Cited by 825
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
Detecting depression and mental illness on social media: an integrative review
Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.07.005 · Cited by 722
Facebook language predicts depression in medical records
Eichstaedt, J. C., Smith, R. J., Merchant, R. M., Ungar, L. H., Crutchley, P., Preoţiuc-Pietro, D., Asch, D. A., & Schwartz, H. A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1802331115 · Cited by 643
Forecasting the onset and course of mental illness with Twitter data
Reece, A. G., Reagan, A. J., Lix, K. L. M., Dodds, P. S., Danforth, C. M., & Langer, E. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-12961-9 · Cited by 401
Cited by
D’Alfonso, S. (2020) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2020.04.005 · Cited by 408
Babu, N. V., & Kanaga, E. G. M. (2022) · SN Computer Science · doi:10.1007/s42979-021-00958-1 · Cited by 303
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
Xu, X., Yao, B., Dong, Y., Gabriel, S., Yu, H., Hendler, J., Ghassemi, M., Dey, A. K., & Wang, D. (2024) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3643540 · Cited by 278
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare
Ji, S., Zhang, T., Ansari, L., Fu, J., Tiwari, P., & Cambria, E. (2022) · Proceedings of the Language Resources and Evaluation Conference · doi:10.63317/2d7umxifj8wz · Cited by 233
What’s in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus
Luccioni, A., & Viviano, J. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) · doi:10.18653/v1/2021.acl-short.24 · Cited by 69
Metadata
- Authors
- Tadesse, M. M., Lin, H., Xu, B., & Yang, L.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 44883-44893
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2927148761
- Cited by
- 463
- References
- 7
- FWCI
- 46.26
- Type
- journal article
- Language
- en
- Open access
- gold
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