{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T20:17:31Z","timestamp":1770063451882,"version":"3.49.0"},"reference-count":30,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Data &amp; Knowledge Engineering"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.datak.2025.102532","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T16:17:28Z","timestamp":1763655448000},"page":"102532","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Optimized adaptive depression state prediction and severity estimation from twitter data"],"prefix":"10.1016","volume":"162","author":[{"given":"Pavani","family":"Chirasani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gatram Rama Mohan","family":"Babu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.datak.2025.102532_bib0001","doi-asserted-by":"crossref","unstructured":"K.K. Leela, R. Surendran, Enhancing accuracy of innovative topic modeling of cinema reviews in Twitter data through Latent Dirichlet Allocation in comparing non-negative matrix factorization, Appl. Math. Sci. Technol. CRC Press, pp. 715\u2013719.","DOI":"10.1201\/9781003606659-139"},{"key":"10.1016\/j.datak.2025.102532_bib0002","series-title":"Deep Learning for Sentiment Analysis with Adaptive Neural Networks for Emotion Classification, 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA)","author":"Jayalakshmi","year":"2024"},{"key":"10.1016\/j.datak.2025.102532_bib0003","series-title":"2022 International Conference on Electronics and Renewable Systems (ICEARS)","article-title":"Improved sentimental analysis to the movie reviews using naive bayes classifier","author":"Gowri","year":"2022"},{"issue":"1","key":"10.1016\/j.datak.2025.102532_bib0004","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1007\/s11063-022-10898-3","article-title":"Author profiling in code-mixed WhatsApp messages using stacked convolution networks and contextualized embedding based text augmentation","volume":"55","author":"Devi","year":"2023","journal-title":"Neural Process. Lett"},{"key":"10.1016\/j.datak.2025.102532_bib0005","doi-asserted-by":"crossref","first-page":"11316","DOI":"10.1109\/ACCESS.2024.3349958","article-title":"The effect of phrase vector embedding in explainable hierarchical attention-based tamil code-mixed hate speech and intent detection","volume":"12","author":"Devi","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.datak.2025.102532_bib0006","unstructured":"D. Sharmila, S. Kannimuthu, G. Ravikumar, K. Anand, KCE DALab-APDA@ FIRE2019: author profiling and deception detection in Arabic using weighted embedding, 2019."},{"key":"10.1016\/j.datak.2025.102532_bib0007","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.jad.2023.10.050","article-title":"Association between depression status and hearing loss among older adults: the role of outdoor activity engagement","volume":"345","author":"Lu","year":"2024","journal-title":"J. Affect. Disord"},{"issue":"2","key":"10.1016\/j.datak.2025.102532_bib0008","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1080\/02673037.2022.2056151","article-title":"Housing quality determinants of depression and suicide ideation by age and gender","volume":"39","author":"Lee","year":"2024","journal-title":"Hous. Stud."},{"issue":"1","key":"10.1016\/j.datak.2025.102532_bib0009","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1186\/s12888-025-06728-0","article-title":"Predicting the treatment outcomes of major depressive disorder interventions with baseline resting-state functional connectivity: a meta-analysis","volume":"25","author":"Zhou","year":"2025","journal-title":"BMC psychiatry"},{"key":"10.1016\/j.datak.2025.102532_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2023.102745","article-title":"Evaluation of deep learning-based depression detection using medical claims data","volume":"147","author":"Bertl","year":"2024","journal-title":"Artif. Intell. Med"},{"key":"10.1016\/j.datak.2025.102532_bib0011","article-title":"Conformal depression prediction","author":"Li","year":"2025","journal-title":"IEEE Trans. Affect. Comput"},{"key":"10.1016\/j.datak.2025.102532_bib0012","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/j.jad.2025.01.060","article-title":"Machine learning-based prediction of illness course in major depression: the relevance of risk factors","volume":"374","author":"Teutenberg","year":"2025","journal-title":"J. Affect. Disord"},{"issue":"1","key":"10.1016\/j.datak.2025.102532_bib0013","article-title":"Prevalence of depression among older adults visiting the Primary Healthcare centers in Jizan City, Saudi Arabia: an analytical cross-sectional study","volume":"16","author":"Alfaifi","year":"2024","journal-title":"Cureus"},{"issue":"1","key":"10.1016\/j.datak.2025.102532_bib0014","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s12888-023-05480-7","article-title":"Decomposing the rural\u2013urban differences in depression among multimorbid older patients in India: evidence from a cross-sectional study","volume":"24","author":"Saha","year":"2024","journal-title":"BMC psychiatry"},{"issue":"3","key":"10.1016\/j.datak.2025.102532_bib0015","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.jadohealth.2023.10.023","article-title":"The role of social networks on depression and anxiety among a sample of urban American Indian\/Alaska Native emerging adults","volume":"74","author":"Dickerson","year":"2024","journal-title":"J. Adolesc. Health"},{"issue":"6","key":"10.1016\/j.datak.2025.102532_bib0016","doi-asserted-by":"crossref","first-page":"6195","DOI":"10.1109\/TCSVT.2025.3533480","article-title":"Depression scale dictionary decomposition framework for multimodal automatic depression level prediction","volume":"35","author":"Niu","year":"2025","journal-title":"IEEE Trans. Circ. Syst. Video Tech"},{"issue":"1","key":"10.1016\/j.datak.2025.102532_bib0017","doi-asserted-by":"crossref","first-page":"e7407","DOI":"10.1002\/cpe.7407","article-title":"Review of automated depression detection: social posts, audio and video, open challenges and future direction","volume":"35","author":"Yadav","year":"2023","journal-title":"Concurr. Comput. Pr. Exp"},{"issue":"4","key":"10.1016\/j.datak.2025.102532_bib0018","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1007\/s13555-023-00909-z","article-title":"Effectiveness of long-Term treatment with brodalumab on anxiety or depressive symptoms in Japanese patients with psoriasis: the ProLOGUE study","volume":"13","author":"Ohata","year":"2023","journal-title":"Dermatol. Thera"},{"issue":"1","key":"10.1016\/j.datak.2025.102532_bib0019","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s13034-023-00703-x","article-title":"Evaluation of a web-based information platform on youth depression and mental health in parents of adolescents with a history of depression","volume":"18","author":"Iglhaut","year":"2024","journal-title":"Child Adolesc. Psychiatry Ment. Health"},{"key":"10.1016\/j.datak.2025.102532_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.102161","article-title":"Transformer-based multimodal feature enhancement networks for multimodal depression detection integrating video, audio and remote photo plethysmograph signals","volume":"104","author":"Fan","year":"2024","journal-title":"Inf. Fusion"},{"issue":"1","key":"10.1016\/j.datak.2025.102532_bib0021","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/s11042-023-15827-7","article-title":"Analysis of region of interest (RoI) of brain for detection of depression using EEG signal","volume":"83","author":"Mahato","year":"2024","journal-title":"Multimed. Tools Appl"},{"issue":"17","key":"10.1016\/j.datak.2025.102532_bib0022","doi-asserted-by":"crossref","first-page":"23649","DOI":"10.1007\/s11042-022-12648-y","article-title":"A hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM","volume":"81","author":"Kour","year":"2022","journal-title":"Multimed. Tools Appl"},{"key":"10.1016\/j.datak.2025.102532_bib0023","article-title":"Deep temporal modelling of clinical depression through social media text","volume":"6","author":"Farruque","year":"2024","journal-title":"Nat. Lang. Process. J"},{"issue":"1","key":"10.1016\/j.datak.2025.102532_bib0024","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s13755-022-00197-5","article-title":"MHA: a multimodal hierarchical attention model for depression detection in social media","volume":"11","author":"Li","year":"2023","journal-title":"Health Inf. Sci. Syst"},{"key":"10.1016\/j.datak.2025.102532_bib0025","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1016\/j.procs.2023.01.141","article-title":"Depression and suicide risk detection on social media using fast text embedding and XG boost classifier","volume":"218","author":"Ghosal","year":"2023","journal-title":"Procedia Comput. Sci"},{"issue":"1","key":"10.1016\/j.datak.2025.102532_bib0026","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11280-021-00992-2","article-title":"Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media","volume":"25","author":"Zogan","year":"2022","journal-title":"World Wide Web"},{"key":"10.1016\/j.datak.2025.102532_bib0027","doi-asserted-by":"crossref","first-page":"49445","DOI":"10.1109\/ACCESS.2022.3172789","article-title":"Zebra optimization algorithm: a new bio-inspired optimization algorithm for solving optimization algorithm","volume":"10","author":"Trojovsk\u00e1","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.datak.2025.102532_bib0028","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.aiopen.2021.06.003","article-title":"Law former: a pre-trained language model for chinese legal long documents","volume":"2","author":"Xiao","year":"2021","journal-title":"AI Open"},{"issue":"17","key":"10.1016\/j.datak.2025.102532_bib0029","doi-asserted-by":"crossref","first-page":"23649","DOI":"10.1007\/s11042-022-12648-y","article-title":"A hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM","volume":"81","author":"Kour","year":"2022","journal-title":"Multimed. Tools Appl"},{"issue":"4","key":"10.1016\/j.datak.2025.102532_bib0030","doi-asserted-by":"crossref","first-page":"4709","DOI":"10.1007\/s11227-021-04040-8","article-title":"Automatic detection of depression symptoms in twitter using multimodal analysis","volume":"78","author":"Safa","year":"2022","journal-title":"J. Supercomput"}],"container-title":["Data &amp; Knowledge Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169023X25001272?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169023X25001272?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T09:06:08Z","timestamp":1770023168000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169023X25001272"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":30,"alternative-id":["S0169023X25001272"],"URL":"https:\/\/doi.org\/10.1016\/j.datak.2025.102532","relation":{},"ISSN":["0169-023X"],"issn-type":[{"value":"0169-023X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Optimized adaptive depression state prediction and severity estimation from twitter data","name":"articletitle","label":"Article Title"},{"value":"Data & Knowledge Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.datak.2025.102532","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"102532"}}