---
title: "A Comprehensive Evaluation of Large Language — Zahran 2025"
description: "The paper asks how well current large language models can spot mental health conditions in Arabic text."
authors: ["Noureldin Zahran", "Aya E. Fouda", "Radwa J. Hanafy", "Mohammed E. Fouda"]
year: 2025
venue: "arXiv:2501.06859"
source: "https://arxiv.org/abs/2501.06859"
doi: "10.48550/arxiv.2501.06859"
arxiv: "2501.06859"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zahran-2025-comprehensive-evaluation-large-language-models/"
---
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# A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context

**Authors:** Noureldin Zahran, Aya E. Fouda, Radwa J. Hanafy, [Mohammed E. Fouda](https://md.research.elamur.ai/free/en/authors/mohammed-e-fouda/) · **Year:** 2025 · **Venue:** arXiv:2501.06859 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2501.06859) · [PDF](https://arxiv.org/pdf/2501.06859) · [DOI 10.48550/arxiv.2501.06859](https://doi.org/10.48550/arxiv.2501.06859) · [arXiv 2501.06859](https://arxiv.org/abs/2501.06859) · [OpenAlex](https://openalex.org/W6979225144)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14) · 7 in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper asks how well current large language models can spot mental health conditions in Arabic text. Annotated Arabic data are scarce, and machine translation may distort results. The authors gathered native Arabic corpora such as DCAT, MCD and AraDepSu, along with English sets like Dreaddit and MedMCQA. They ran 8 multilingual and bilingual LLMs across binary, multi-class and severity tasks. Their controlled design varied three things: prompt structure (two zero-shot templates, ZS-1 and ZS-2), language (native data versus translations in both directions) and few-shot prompting (FS-2). Prompt wording mattered most, largely because models drifted from the requested output format. Language had a smaller effect than model choice. Phi-3.5 MoE gave the best balanced accuracy and Mistral NeMo the lowest error on severity scales. Few-shot examples helped consistently. This is the first broad benchmark of its kind for Arabic social media. It shows that output-format compliance and translation artefacts can distort LLM evaluations as much as true model ability does.

## Key points
- On multi-class datasets, the more structured ZS-2 template beat ZS-1 by about 14.5 points of balanced accuracy (BA), mainly through fewer unparseable answers.
- Phi-3.5 MoE led on BA, especially for binary tasks, with Gemma 2 9B second. Mistral NeMo clearly won on mean absolute error (MAE) for severity prediction.
- Moving from ZS-2 to FS-2 raised GPT-4o Mini's BA by roughly 20% on average and by 57.6% on multi-class sets. Phi-3.5 MoE gained less.
- Running the English configuration improved Arabic results by about 6% BA on average. When Arabic data were machine-translated to English under Arabic instructions, the original Arabic scored higher, by up to 17.3 BA on binary sets.
- Datasets differed sharply in difficulty. DCAT was easiest, while SDCNL Depression stayed close to chance level.

## Interesting ideas
- The authors deliberately included weakly documented datasets, planning to use agreement across many LLMs to reveal suspicious labels. A dataset where every model scores near chance is itself a red flag for its annotation. — For Mova (learn): Mova could run its witness parsers over a treebank and use their joint disagreement to flag likely gold-annotation errors, and to flag suspect silver batches before training.
- They translated in both directions and kept the instruction language fixed separately from the data language. This helps separate a genuine language effect from damage done by translation. — For Mova (learn): when English material is produced by translation, Mova could add a control that holds the pipeline fixed and swaps only the text's origin, so translationese is not mistaken for parser or reader weakness.
- Invalid or unparseable responses were counted and analysed as a separate outcome, not silently treated as wrong answers. This made clear that prompt sensitivity was really an instruction-following problem. — For Mova (learn): LLM teacher tiers could log format-violation rates per prompt and per model, so a bad teacher prompt is caught before its outputs become silver labels.
- The metric matched the task shape: balanced accuracy for categorical labels, MAE for ordinal severity scales. Near misses on a scale then earn partial credit. — For Mova (answer): graded answers such as quantities, ranks or rating-like judgements could be scored by distance instead of exact match, making evaluation of word problems and test readers more informative.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{zahran2025comprehensive,
  title = {{A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context}},
  author = {Zahran, Noureldin and Fouda, Aya E. and Hanafy, Radwa J. and Fouda, Mohammed E.},
  year = {2025},
  doi = {10.48550/arxiv.2501.06859},
  eprint = {2501.06859},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2501.06859}
}
```

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## References

38 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. World Health Organization. Depression, 2023. Accessed: September 14, 2024. [find](https://scholar.google.com/scholar?q=World+Health+Organization%2E+Depression%2C+2023%2E+Accessed%3A+September+14%2C+2024%2E)
2. World Health Organization. Anxiety disorders, 2022. Accessed: 2024-09-26. [find](https://scholar.google.com/scholar?q=World+Health+Organization%2E+Anxiety+disorders%2C+2022%2E+Accessed%3A+2024%2D09%2D26%2E)
3. World Health Organization. World Mental Health Report: Transforming Mental Health for All. World Health Organization, Geneva, 2022. Accessed: 2024-09-26. [find](https://scholar.google.com/scholar?q=World+Health+Organization%2E+World+Mental+Health+Report%3A+Transforming+Mental+Health+for+All%2E+World+Health+Organization%2C+Geneva%2C+2022%2E+Accessed%3A+2024%2D09%2D26%2E)
4. Kathrin Thomas. Challenges of health care in the middle east and north africa. [https://www.arabbarometer.org/wp-content/uploads/ABV_Health_Report_FINAL_Public-Opinion-2019.pdf](https://www.arabbarometer.org/wp-content/uploads/ABV_Health_Report_FINAL_Public-Opinion-2019.pdf), 2019. Arab Barometer – Wave V, Topic Report – Health Care. [↗](https://www.arabbarometer.org/wp-content/uploads/ABV_Health_Report_FINAL_Public-Opinion-2019.pdf)
5. Ehab Salah Eshak and Tarek Ahmed Abd-El Rahman. Depression in public servants of upper egypt: Gender-specific prevalence and determining factors. Journal of Prevention, 43:623–638, 2022. [find](https://scholar.google.com/scholar?q=Ehab+Salah+Eshak+and+Tarek+Ahmed+Abd%2DEl+Rahman%2E+Depression+in+public+servants+of+upper+egypt%3A+Gender%2Dspecific+prevalence+and+determining+factors%2E+Journal+of+Prevention%2C+43%3A623%E2%80%93638%2C+2022%2E)
6. World Health Organization. Depression and other common mental disorders: Global health estimates. [https://iris.who.int/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf?sequence=1](https://iris.who.int/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf?sequence=1), 2017. Licence: CC BY-NC-SA 3.0 IGO. [↗](https://iris.who.int/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf?sequence=1)
7. World Health Organization. Mental health atlas 2020 - member state profile: Egypt. [https://cdn.who.int/media/docs/default-source/mental-health/mental-health-atlas-2020-country-profiles/egy.pdf?sfvrsn=4c708821_6&download=true](https://cdn.who.int/media/docs/default-source/mental-health/mental-health-atlas-2020-country-profiles/egy.pdf?sfvrsn=4c708821_6&download=true), 2020. Licence: CC BY-NC-SA 3.0 IGO. [↗](https://cdn.who.int/media/docs/default-source/mental-health/mental-health-atlas-2020-country-profiles/egy.pdf?sfvrsn=4c708821_6&download=true)
8. World Health Organization. Mental health atlas. [https://iris.who.int/bitstream/handle/10665/345946/9789240036703-eng.pdf?sequence=1](https://iris.who.int/bitstream/handle/10665/345946/9789240036703-eng.pdf?sequence=1), 2020. [↗](https://iris.who.int/bitstream/handle/10665/345946/9789240036703-eng.pdf?sequence=1)
9. Munmun De Choudhury and Sushovan De. Mental health discourse on reddit: Self-disclosure, social support, and anonymity. Proceedings of the International AAAI Conference on Web and Social Media, 8(1):71–80, May 2014. [find](https://scholar.google.com/scholar?q=Munmun+De+Choudhury+and+Sushovan+De%2E+Mental+health+discourse+on+reddit%3A+Self%2Ddisclosure%2C+social+support%2C+and+anonymity%2E+Proceedings+of+the+International+AAAI+Conference+on+Web+and+Social+Media%2C+8%281%29%3A7)
10. Sharath Chandra Guntuku, David B Yaden, Margaret L Kern, Lyle H Ungar, and Johannes C Eichstaedt. Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences, 18:43–49, 2017. Big data in the behavioural sciences. [find](https://scholar.google.com/scholar?q=Sharath+Chandra+Guntuku%2C+David+B+Yaden%2C+Margaret+L+Kern%2C+Lyle+H+Ungar%2C+and+Johannes+C+Eichstaedt%2E+Detecting+depression+and+mental+illness+on+social+media%3A+an+integrative+review%2E+Current+Opinion+in+Beh)
11. Glen Coppersmith, Mark Dredze, and Craig Harman. Quantifying mental health signals in Twitter. In Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pages 51–60, Baltimore, Maryland, USA, June 2014. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Glen+Coppersmith%2C+Mark+Dredze%2C+and+Craig+Harman%2E+Quantifying+mental+health+signals+in+Twitter%2E+In+Proceedings+of+the+Workshop+on+Computational+Linguistics+and+Clinical+Psychology%3A+From+Linguistic+Sign) [our card](https://md.research.elamur.ai/free/en/papers/coppersmith-2014-quantifying-mental-health-signals-twitter/)
12. M Baklola, M Terra, A Taha, M Elnemr, M Yaseen, A Maher, AH Buzaid, R Alenazi, SA Osman Mohamed, D Abdelhady, and AH El-Gilany. Mental health literacy and help-seeking behaviour among egyptian undergraduates: a cross-sectional national study. BMC Psychiatry, 24(1):202, 2024. [find](https://scholar.google.com/scholar?q=M+Baklola%2C+M+Terra%2C+A+Taha%2C+M+Elnemr%2C+M+Yaseen%2C+A+Maher%2C+AH+Buzaid%2C+R+Alenazi%2C+SA+Osman+Mohamed%2C+D+Abdelhady%2C+and+AH+El%2DGilany%2E+Mental+health+literacy+and+help%2Dseeking+behaviour+among+egyptian+undergr)
13. Aziliz Le Glaz, Yannis Haralambous, Deok-Hee Kim-Dufor, Philippe Lenca, Romain Billot, Taylor C Ryan, Jonathan Marsh, Jordan Devylder, Michel Walter, Sofian Berrouiguet, et al. Machine learning and natural language processing in mental health: systematic review. Journal of Medical Internet Research, 23(5):e15708, 2021. [find](https://scholar.google.com/scholar?q=Aziliz+Le+Glaz%2C+Yannis+Haralambous%2C+Deok%2DHee+Kim%2DDufor%2C+Philippe+Lenca%2C+Romain+Billot%2C+Taylor+C+Ryan%2C+Jonathan+Marsh%2C+Jordan+Devylder%2C+Michel+Walter%2C+Sofian+Berrouiguet%2C+et+al%2E+Machine+learning+and+na)
14. Josh Achiam, Steven Adler, Sandhini Agarwal, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. [↗](https://arxiv.org/abs/2303.08774) [our card](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/)
15. Rafael A Calvo, David N Milne, M Hussain, and Helen Christensen. Natural language processing in mental health applications using large-scale text analysis. Journal of Medical Internet Research, 19(5):e240, 2017. [find](https://scholar.google.com/scholar?q=Rafael+A+Calvo%2C+David+N+Milne%2C+M+Hussain%2C+and+Helen+Christensen%2E+Natural+language+processing+in+mental+health+applications+using+large%2Dscale+text+analysis%2E+Journal+of+Medical+Internet+Research%2C+19%285%29%3A)
16. Kathleen K Fitzpatrick, Alison Darcy, and Molly Vierhile. Delivering cognitive behavioral therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (woebot): A randomized controlled trial. JMIR mental health, 4(2):e19, 2017. [find](https://scholar.google.com/scholar?q=Kathleen+K+Fitzpatrick%2C+Alison+Darcy%2C+and+Molly+Vierhile%2E+Delivering+cognitive+behavioral+therapy+to+young+adults+with+symptoms+of+depression+and+anxiety+using+a+fully+automated+conversational+agent+%28)
17. Cynthia Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1:206–215, 2019. [find](https://scholar.google.com/scholar?q=Cynthia+Rudin%2E+Stop+explaining+black+box+machine+learning+models+for+high+stakes+decisions+and+use+interpretable+models+instead%2E+Nature+Machine+Intelligence%2C+1%3A206%E2%80%93215%2C+2019%2E) [our card](https://md.research.elamur.ai/free/en/papers/rudin-2018-stop-explaining-black-box/)
18. Abdelrahman Hanafi, Mohammed Saad, Noureldin Zahran, Radwa Hanafy, and Mohammed Fouda. A comprehensive evaluation of large language models on mental illnesses. arXiv preprint, 2024. [find](https://scholar.google.com/scholar?q=Abdelrahman+Hanafi%2C+Mohammed+Saad%2C+Noureldin+Zahran%2C+Radwa+Hanafy%2C+and+Mohammed+Fouda%2E+A+comprehensive+evaluation+of+large+language+models+on+mental+illnesses%2E+arXiv+preprint%2C+2024%2E)
19. Xuhai Xu, Bingsheng Yao, Yuanzhe Dong, Saadia Gabriel, Hong Yu, James Hendler, Marzyeh Ghassemi, Anind K Dey, and Dakuo Wang. Mental-llm: Leveraging large language models for mental health prediction via online text data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 8(1):1–32, 2024. [find](https://scholar.google.com/scholar?q=Xuhai+Xu%2C+Bingsheng+Yao%2C+Yuanzhe+Dong%2C+Saadia+Gabriel%2C+Hong+Yu%2C+James+Hendler%2C+Marzyeh+Ghassemi%2C+Anind+K+Dey%2C+and+Dakuo+Wang%2E+Mental%2Dllm%3A+Leveraging+large+language+models+for+mental+health+prediction+)
20. Sara Pieri, Sahal Shaji Mullappilly, Fahad Shahbaz Khan, Rao Muhammad Anwer, Salman Khan, Timothy Baldwin, and Hisham Cholakkal. Bimedix: Bilingual medical mixture of experts llm. arXiv preprint arXiv:2402.13253, 2024. [↗](https://arxiv.org/abs/2402.13253)
21. Anis Koubaa, Adel Ammar, Lahouari Ghouti, Omar Najar, and Serry Sibaee. Arabiangpt: Native arabic gpt-based large language model, 2024. [find](https://scholar.google.com/scholar?q=Anis+Koubaa%2C+Adel+Ammar%2C+Lahouari+Ghouti%2C+Omar+Najar%2C+and+Serry+Sibaee%2E+Arabiangpt%3A+Native+arabic+gpt%2Dbased+large+language+model%2C+2024%2E) [our card](https://md.research.elamur.ai/free/en/papers/koubaa-2024-arabiangpt-native-arabic-gpt-based/)
22. Radwa Nassar, AbdelMoniem Helmy, and Nagy Ramadan. Depression corpus of arabic tweets, 2022. [find](https://scholar.google.com/scholar?q=Radwa+Nassar%2C+AbdelMoniem+Helmy%2C+and+Nagy+Ramadan%2E+Depression+corpus+of+arabic+tweets%2C+2022%2E)
23. Ashwag Maghraby and Hosnia Ali. Modern standard arabic mood changing and depression dataset. Data in Brief, 41:107999, 2022. [find](https://scholar.google.com/scholar?q=Ashwag+Maghraby+and+Hosnia+Ali%2E+Modern+standard+arabic+mood+changing+and+depression+dataset%2E+Data+in+Brief%2C+41%3A107999%2C+2022%2E)
24. Mariam Hassib, Nancy Hossam, Jolie Sameh, and Marwan Torki. Aradepsu: Detecting depression and suicidal ideation in arabic tweets using transformers. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 302–311. Association for Computational Linguistics, 2022. [find](https://scholar.google.com/scholar?q=Mariam+Hassib%2C+Nancy+Hossam%2C+Jolie+Sameh%2C+and+Marwan+Torki%2E+Aradepsu%3A+Detecting+depression+and+suicidal+ideation+in+arabic+tweets+using+transformers%2E+In+Proceedings+of+the+Seventh+Arabic+Natural+Langu)
25. Mohammad El-Ramly, Hager Abu-Elyazid, Youseef Mo’men, Gameel Alshaer, Nardine Adib, Kareem Alaa Eldeen, and Mariam El-Shazly. Cairodep: Detecting depression in arabic posts using bert transformers. In 2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS), pages 207–212, 2021. [find](https://scholar.google.com/scholar?q=Mohammad+El%2DRamly%2C+Hager+Abu%2DElyazid%2C+Youseef+Mo%E2%80%99men%2C+Gameel+Alshaer%2C+Nardine+Adib%2C+Kareem+Alaa+Eldeen%2C+and+Mariam+El%2DShazly%2E+Cairodep%3A+Detecting+depression+in+arabic+posts+using+bert+transformers%2E+In)
26. Arwa Diwali. Transfer learning-based automatic sentiment annotation of a twitter-based arabic mental illness (ami) dataset, 2023. [find](https://scholar.google.com/scholar?q=Arwa+Diwali%2E+Transfer+learning%2Dbased+automatic+sentiment+annotation+of+a+twitter%2Dbased+arabic+mental+illness+%28ami%29+dataset%2C+2023%2E)
27. Amany Adel Rofaida, Rofaida Ehab, Eman Eiad Nosiba, Esraa Mahmoud, and Norhan Abdul Menaem. Mental health supportive application. [https://github.com/esraa-mahmoudsaid/Classification-of-Mental-Disorders-in-Egyptian-dialect-of-Arabic](https://github.com/esraa-mahmoudsaid/Classification-of-Mental-Disorders-in-Egyptian-dialect-of-Arabic), 2022. Graduation Project, Department of Medical Informatics, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Egypt. Supervised by Prof. Hossam Moftah and TA Ehab Ibrahim. [↗](https://github.com/esraa-mahmoudsaid/Classification-of-Mental-Disorders-in-Egyptian-dialect-of-Arabic)
28. NLLB Team. No language left behind: Scaling human-centered machine translation. arXiv.2207.04672, 2022. [find](https://scholar.google.com/scholar?q=NLLB+Team%2E+No+language+left+behind%3A+Scaling+human%2Dcentered+machine+translation%2E+arXiv%2E2207%2E04672%2C+2022%2E)
29. Norah Saleh Alghamdi, Hanan A. Hosni Mahmoud, Ajith Abraham, Samar Awadh Alanazi, and Laura García-Hernández. Predicting depression symptoms in an arabic psychological forum. IEEE Access, 8:57317–57334, 2020. [find](https://scholar.google.com/scholar?q=Norah+Saleh+Alghamdi%2C+Hanan+A%2E+Hosni+Mahmoud%2C+Ajith+Abraham%2C+Samar+Awadh+Alanazi%2C+and+Laura+Garc%C3%ADa%2DHern%C3%A1ndez%2E+Predicting+depression+symptoms+in+an+arabic+psychological+forum%2E+IEEE+Access%2C+8%3A57317%E2%80%935733)
30. Salma Almouzini, Maher khemakhem, and Asem Alageel. Detecting arabic depressed users from twitter data. Procedia Computer Science, 163:257–265, 2019. 16th Learning and Technology Conference 2019Artificial Intelligence and Machine Learning: Embedding the Intelligence. [find](https://scholar.google.com/scholar?q=Salma+Almouzini%2C+Maher+khemakhem%2C+and+Asem+Alageel%2E+Detecting+arabic+depressed+users+from+twitter+data%2E+Procedia+Computer+Science%2C+163%3A257%E2%80%93265%2C+2019%2E+16th+Learning+and+Technology+Conference+2019Artifi)
31. Abdulqader M. Almars. Attention-based bi-lstm model for arabic depression classification. Computers, Materials & Continua, 71(2):3091–3106, 2022. [find](https://scholar.google.com/scholar?q=Abdulqader+M%2E+Almars%2E+Attention%2Dbased+bi%2Dlstm+model+for+arabic+depression+classification%2E+Computers%2C+Materials+%26+Continua%2C+71%282%29%3A3091%E2%80%933106%2C+2022%2E)
32. Eatedal Alabdulkreem. Prediction of depressed arab women using their tweets. Journal of Decision Systems, 30(2-3):102–117, 2021. [find](https://scholar.google.com/scholar?q=Eatedal+Alabdulkreem%2E+Prediction+of+depressed+arab+women+using+their+tweets%2E+Journal+of+Decision+Systems%2C+30%282%2D3%29%3A102%E2%80%93117%2C+2021%2E)
33. Elsbeth Turcan and Kathleen McKeown. Dreaddit: A reddit dataset for stress analysis in social media. arXiv preprint arXiv:1911.00133, 2019. [↗](https://arxiv.org/abs/1911.00133)
34. Ayaan Haque, Viraaj Reddi, and Tyler Giallanza. Deep learning for suicide and depression identification with unsupervised label correction. In Artificial Neural Networks and Machine Learning–ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part V 30, pages 436–447. Springer, 2021. [find](https://scholar.google.com/scholar?q=Ayaan+Haque%2C+Viraaj+Reddi%2C+and+Tyler+Giallanza%2E+Deep+learning+for+suicide+and+depression+identification+with+unsupervised+label+correction%2E+In+Artificial+Neural+Networks+and+Machine+Learning%E2%80%93ICANN+202)
35. Matthew Louis Mauriello, Thierry Lincoln, Grace Hon, Dorien Simon, Dan Jurafsky, and Pablo Paredes. Sad: A stress annotated dataset for recognizing everyday stressors in sms-like conversational systems. In Extended abstracts of the 2021 CHI conference on human factors in computing systems, pages 1–7, 2021. [find](https://scholar.google.com/scholar?q=Matthew+Louis+Mauriello%2C+Thierry+Lincoln%2C+Grace+Hon%2C+Dorien+Simon%2C+Dan+Jurafsky%2C+and+Pablo+Paredes%2E+Sad%3A+A+stress+annotated+dataset+for+recognizing+everyday+stressors+in+sms%2Dlike+conversational+system)
36. Mohsinul Kabir, Tasnim Ahmed, Md Bakhtiar Hasan, Md Tahmid Rahman Laskar, Tarun Kumar Joarder, Hasan Mahmud, and Kamrul Hasan. Deptweet: A typology for social media texts to detect depression severities. Computers in Human Behavior, 139:107503, 2023. [find](https://scholar.google.com/scholar?q=Mohsinul+Kabir%2C+Tasnim+Ahmed%2C+Md+Bakhtiar+Hasan%2C+Md+Tahmid+Rahman+Laskar%2C+Tarun+Kumar+Joarder%2C+Hasan+Mahmud%2C+and+Kamrul+Hasan%2E+Deptweet%3A+A+typology+for+social+media+texts+to+detect+depression+severiti)
37. Kayalvizhi Sampath and Thenmozhi Durairaj. Data set creation and empirical analysis for detecting signs of depression from social media postings. In International Conference on Computational Intelligence in Data Science, pages 136–151. Springer, 2022. [find](https://scholar.google.com/scholar?q=Kayalvizhi+Sampath+and+Thenmozhi+Durairaj%2E+Data+set+creation+and+empirical+analysis+for+detecting+signs+of+depression+from+social+media+postings%2E+In+International+Conference+on+Computational+Intellige)
38. S Kayalvizhi, Thenmozhi Durairaj, Bharathi Raja Chakravarthi, et al. Findings of the shared task on detecting signs of depression from social media. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 331–338, 2022. [find](https://scholar.google.com/scholar?q=S+Kayalvizhi%2C+Thenmozhi+Durairaj%2C+Bharathi+Raja+Chakravarthi%2C+et+al%2E+Findings+of+the+shared+task+on+detecting+signs+of+depression+from+social+media%2E+In+Proceedings+of+the+Second+Workshop+on+Language+T)