Depression Detection Among Social Media Users Using Machine Learning
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Verma et al. [113] found that the Random Forest model achieved a peak accuracy of 78% for depression detection from tweets.
“Of these, the Random Forest model demonstrated superior performance, achieving an impressive accuracy peak of 78%.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [113]
Cites
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Natural language processing applied to mental illness detection: a narrative review
Zhang, T., Schoene, A. M., Ji, S., & Ananiadou, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00589-7 · Cited by 476 · see citing works
A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works
- Authors
- Verma, P., Sharma, K., & Walia, G. S.
- Affiliations
- Delhi Technological University (IN)
- Defence Research and Development Organisation (IN)
- Corresponding author
- Verma, Prashant
- Venue
- Advances in Intelligent Systems and Computing
- Publisher
- Springer Singapore
- Published
- 2020-08-02
- Pages
- 865-874
- ISSN
- 2194-5357 (ISSN-L)
- 2194-5365
- OpenAlex
- W3046477338
- Cited by
- 10
- References
- 13
- Citations by year
- 2025: 2 · 2024: 4 · 2022: 3 · 2020: 1
- FWCI
- 6.05
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Advances in intelligent systems and computing (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- depression detection, social media, sentiment analysis, user mood, mental health monitoring, online communication
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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