The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Depression detection from social network data using machine learning techniques
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods
Tausczik, Y. R., & Pennebaker, J. W. (2010) · Journal of Language and Social Psychology · doi:10.1177/0261927x09351676 · Cited by 5,774
Scott, J. (2017) · doi:10.4135/9781529716597 · Cited by 879
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
Personality and patterns of Facebook usage
Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., & Stillwell, D. (2012) · Proceedings of the 4th Annual ACM Web Science Conference · doi:10.1145/2380718.2380722 · Cited by 380
Social media, big data, and mental health: current advances and ethical implications
Conway, M., & O’Connor, D. (2016) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2016.01.004 · Cited by 231
Cited by
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
Babu, N. V., & Kanaga, E. G. M. (2022) · SN Computer Science · doi:10.1007/s42979-021-00958-1 · Cited by 303
Data Integration Challenges for Machine Learning in Precision Medicine
Martínez-García, M., & Hernández-Lemus, E. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.784455 · Cited by 185
Richter, T., Fishbain, B., Richter-Levin, G., & Okon-Singer, H. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11100957 · Cited by 52
Explainable depression symptom detection in social media
Bao, E., Pérez, A., & Parapar, J. (2024) · Health Information Science and Systems · doi:10.1007/s13755-024-00303-9 · Cited by 46
Metadata
- Authors
- Islam, M. R., Kabir, M. A., Ahmed, A., Kamal, A. R. M., Wang, H., & Ulhaq, A.
- Venue
- Health Information Science and Systems
- Published
- 2018-08-27
- Volume
- 6
- Issue
- 1
- Pages
- 8-8
- ISSN
- 2047-2501 (ISSN-L)
- OpenAlex
- W2889097229
- Cited by
- 465
- References
- 5
- FWCI
- 33.51
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Depression detection from social network data using machine learning techniques. https://app.cochat.ai/papers/10.1007/s13755-018-0046-0
- The paper
- Islam, M. R., Kabir, M. A., Ahmed, A., Kamal, A. R. M., Wang, H., & Ulhaq, A. (2018). Depression detection from social network data using machine learning techniques. Health Information Science and Systems. https://doi.org/10.1007/s13755-018-0046-0
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
