{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T06:35:16Z","timestamp":1783233316542,"version":"3.54.6"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soc. Netw. Anal. Min."],"DOI":"10.1007\/s13278-026-01589-1","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T06:37:32Z","timestamp":1772001452000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A comparative and statistical analysis of depression classification in social media: assessing the impact of temporal boundaries and text representations"],"prefix":"10.1007","volume":"16","author":[{"given":"Miryam Elizabeth","family":"Villa-P\u00e9rez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karla Mar\u00eda","family":"Valencia-Segura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniela","family":"Moctezuma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis","family":"Villase\u00f1or-Pineda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis A.","family":"Trejo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"issue":"1","key":"1589_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2025.104342","volume":"63","author":"PN Ahmad","year":"2026","unstructured":"Ahmad PN, Shah AM, Lee K, Muhammad W (2026) Misinformation detection on online social networks using pretrained language models. Inf Process Manage 63(1):104342","journal-title":"Inf Process Manage"},{"issue":"22","key":"1589_CR2","doi-asserted-by":"publisher","first-page":"20149","DOI":"10.1007\/s00521-022-07569-8","volume":"34","author":"ME Arag\u00f3n","year":"2022","unstructured":"Arag\u00f3n ME, L\u00f3pez-Monroy AP, Gonz\u00e1lez LC, Montes-y-G\u00f3mez M (2022) Approaching what and how people with mental disorders communicate in social media-introducing a multi-channel representation. Neural Comput Appl 34(22):20149\u201320164. https:\/\/doi.org\/10.1007\/s00521-022-07569-8","journal-title":"Neural Comput Appl"},{"key":"1589_CR3","unstructured":"Arevalo J, Solorio T, Montes-y-G\u00f3mez M, Gonz\u00e1lez FA (2017) Gated multimodal units for information fusion. arXiv preprint arXiv:1702.01992"},{"key":"1589_CR4","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.specom.2022.03.002","volume":"140","author":"BT Atmaja","year":"2022","unstructured":"Atmaja BT, Sasou A, Akagi M (2022) Survey on bimodal speech emotion recognition from acoustic and linguistic information fusion. Speech Commun 140:11\u201328","journal-title":"Speech Commun"},{"issue":"2","key":"1589_CR5","first-page":"102","volume":"5","author":"B Bharadwaj","year":"2025","unstructured":"Bharadwaj B, Nayak S, Panigrahi PK (2025) Sentiment analysis for identifying depression through social media texts using machine learning technique. Big Data comput vis 5(2):102\u2013118","journal-title":"Big Data comput vis"},{"issue":"6","key":"1589_CR6","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/s00138-021-01249-8","volume":"32","author":"SY Boulahia","year":"2021","unstructured":"Boulahia SY, Amamra A, Madi MR, Daikh S (2021) Early, intermediate and late fusion strategies for robust deep learning-based multimodal action recognition. Mach Vis Appl 32(6):121","journal-title":"Mach Vis Appl"},{"key":"1589_CR7","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.eswa.2019.05.023","volume":"133","author":"SG Burdisso","year":"2019","unstructured":"Burdisso SG, Errecalde M, Montes-y-G\u00f3mez MA (2019) text classification framework for simple and effective early depression detection over social media streams. Expert Syst Appl 133:182\u2013197. https:\/\/doi.org\/10.1016\/j.eswa.2019.05.023","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1589_CR8","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1038\/s41746-020-0233-7","volume":"3","author":"S Chancellor","year":"2020","unstructured":"Chancellor S, De Choudhury M (2020) Methods in predictive techniques for mental health status on social media: a critical review. NPJ digit med 3(1):43","journal-title":"NPJ digit med"},{"issue":"1","key":"1589_CR9","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1038\/s44184-024-00107-5","volume":"3","author":"R Chandrasekaran","year":"2024","unstructured":"Chandrasekaran R, Kotaki S, Nagaraja AH (2024) Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study. npj Mental Health Res 3(1):62. https:\/\/doi.org\/10.1038\/s44184-024-00107-5","journal-title":"npj Mental Health Res"},{"key":"1589_CR10","doi-asserted-by":"publisher","unstructured":"Chen X, Sykora MD, Jackson TW, Elayan S (2018) What about mood swings: Identifying depression on twitter with temporal measures of emotions. In: Companion Proceedings of the The Web Conference 2018. WWW \u201918, pp. 1653\u20131660. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE . https:\/\/doi.org\/10.1145\/3184558.3191624","DOI":"10.1145\/3184558.3191624"},{"key":"1589_CR11","doi-asserted-by":"crossref","unstructured":"Chen X, Sykor M, Jackson T, Elayan S, Munir F (2018) Tweeting your mental health: An exploration of different classifiers and features with emotional signals in identifying mental health conditions","DOI":"10.24251\/HICSS.2018.421"},{"key":"1589_CR12","doi-asserted-by":"publisher","unstructured":"Coppersmith G, Dredze M, Harman C, Hollingshead K, Mitchell M (2015) Clpsych 2015 shared task: Depression and ptsd on twitter. In: Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pp. 31\u201339. Association for Computational Linguistics, https:\/\/doi.org\/10.3115\/v1\/W15-1204","DOI":"10.3115\/v1\/W15-1204"},{"key":"1589_CR13","doi-asserted-by":"crossref","unstructured":"Couto M, Perez A, Parapar J, Losada DE (2025) Temporal word embeddings for early detection of psychological disorders on social media. Journal of Healthcare Informatics Research, 1\u201330","DOI":"10.1007\/s41666-025-00186-9"},{"issue":"8","key":"1589_CR14","doi-asserted-by":"publisher","first-page":"37818","DOI":"10.2196\/37818","volume":"10","author":"B Cui","year":"2022","unstructured":"Cui B, Wang J, Lin H, Zhang Y, Yang L, Xu B (2022) Emotion-based reinforcement attention network for depression detection on social media: Algorithm development and validation. JMIR Med Inform 10(8):37818. https:\/\/doi.org\/10.2196\/37818","journal-title":"JMIR Med Inform"},{"key":"1589_CR15","unstructured":"Depressive Disorder (depression). World Health Organization. Available at https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/depression"},{"issue":"22","key":"1589_CR16","doi-asserted-by":"publisher","first-page":"10932","DOI":"10.3390\/app112210932","volume":"11","author":"LM Gallegos Salazar","year":"2021","unstructured":"Gallegos Salazar LM, Loyola-Gonz\u00e1lez O, Medina-P\u00e9rez MA (2021) An explainable approach based on emotion and sentiment features for detecting people with mental disorders on social networks. Appl Sci 11(22):10932. https:\/\/doi.org\/10.3390\/app112210932","journal-title":"Appl Sci"},{"key":"1589_CR17","unstructured":"Global Health Data Exchange (GHDx). Institute of Health Metrics and Evaluation. Available at https:\/\/vizhub.healthdata.org\/gbd-results\/"},{"issue":"1","key":"1589_CR18","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MCI.2019.2954668","volume":"15","author":"M Graff","year":"2020","unstructured":"Graff M, Miranda-Jimenez S, Tellez ES, Moctezuma D (2020) Evomsa: A multilingual evolutionary approach for sentiment analysis [application notes]. IEEE Comput Intell Mag 15(1):76\u201388. https:\/\/doi.org\/10.1109\/MCI.2019.2954668","journal-title":"IEEE Comput Intell Mag"},{"issue":"1","key":"1589_CR19","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1609\/icwsm.v8i1.14550","volume":"8","author":"C Hutto","year":"2014","unstructured":"Hutto C, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proc Int AAAI Conf Weblogs Soc Media 8(1):216\u2013225. https:\/\/doi.org\/10.1609\/icwsm.v8i1.14550","journal-title":"Proc Int AAAI Conf Weblogs Soc Media"},{"key":"1589_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2022.107503","volume":"139","author":"M Kabir","year":"2023","unstructured":"Kabir M, Ahmed T, Hasan MB, Laskar MTR, Joarder TK, Mahmud H, Hasan K (2023) Deptweet: a typology for social media texts to detect depression severities. Comput Hum Behav 139:107503","journal-title":"Comput Hum Behav"},{"issue":"5","key":"1589_CR21","doi-asserted-by":"publisher","first-page":"0322299","DOI":"10.1371\/journal.pone.0322299","volume":"20","author":"G Lorenzoni","year":"2025","unstructured":"Lorenzoni G, Tavares C, Nascimento N, Alencar P, Cowan D (2025) Assessing ml classification algorithms and nlp techniques for depression detection: an experimental case study. PLoS ONE 20(5):0322299","journal-title":"PLoS ONE"},{"key":"1589_CR22","doi-asserted-by":"crossref","unstructured":"MacAvaney S, Desmet B, Cohan A, Soldaini L, Yates A, Zirikly A, Goharian N (2018) Rsdd-time: Temporal annotation of self-reported mental health diagnoses. In: Loveys, K., Niederhoffer, K., Prud\u2019hommeaux, E., Resnik, R., Resnik, P. (eds.) Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pp. 168\u2013173. Association for Computational Linguistics, New Orleans, LA. https:\/\/doi.org\/10.18653\/v1\/W18-0618","DOI":"10.18653\/v1\/W18-0618"},{"issue":"3","key":"1589_CR23","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1111\/j.1467-8640.2012.00460.x","volume":"29","author":"SM Mohammad","year":"2013","unstructured":"Mohammad SM, Turney PD (2013) Crowdsourcing a word-emotion association lexicon. Comput Intell 29(3):436\u2013465","journal-title":"Comput Intell"},{"key":"1589_CR24","doi-asserted-by":"crossref","unstructured":"Mohammad S, Bravo-Marquez F, Salameh M, Kiritchenko S (2018) Semeval-2018 task 1: Affect in tweets. In: Proceedings of the 12th International Workshop on Semantic Evaluation, pp. 1\u201317","DOI":"10.18653\/v1\/S18-1001"},{"key":"1589_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2024.100654","volume":"53","author":"A Montejo-R\u00e1ez","year":"2024","unstructured":"Montejo-R\u00e1ez A, Molina-Gonz\u00e1lez MD, Jim\u00e9nez-Zafra SM, Garc\u00eda-Cumbreras M\u00c1, Garc\u00eda-L\u00f3pez LJ (2024) A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges. Comput Sci Rev 53:100654. https:\/\/doi.org\/10.1016\/j.cosrev.2024.100654","journal-title":"Comput Sci Rev"},{"key":"1589_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2024.100654","volume":"53","author":"A Montejo-R\u00e1ez","year":"2024","unstructured":"Montejo-R\u00e1ez A, Molina-Gonz\u00e1lez MD, Jim\u00e9nez-Zafra SM, Garc\u00eda-Cumbreras M\u00c1, Garc\u00eda-L\u00f3pez LJ (2024) A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges. Comput Sci Rev 53:100654","journal-title":"Comput Sci Rev"},{"key":"1589_CR27","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.patrec.2024.03.010","volume":"186","author":"S Nava-Mu\u00f1oz","year":"2024","unstructured":"Nava-Mu\u00f1oz S, Graff M, Escalante HJ (2024) Analysis of systems\u2019 performance in natural language processing competitions. Pattern Recogn Lett 186:346\u2013353. https:\/\/doi.org\/10.1016\/j.patrec.2024.03.010","journal-title":"Pattern Recogn Lett"},{"key":"1589_CR28","doi-asserted-by":"publisher","first-page":"59225","DOI":"10.2196\/59225","volume":"26","author":"D Owen","year":"2024","unstructured":"Owen D, Lynham AJ, Smart SE, Pardi\u00f1as AF, Camacho Collados J (2024) Ai for analyzing mental health disorders among social media users: Quarter-century narrative review of progress and challenges. J Med Internet Res 26:59225. https:\/\/doi.org\/10.2196\/59225","journal-title":"J Med Internet Res"},{"key":"1589_CR29","unstructured":"Pennebaker J, Boyd R, Jordan K, Blackburn K (2015) The development and psychometric properties of liwc2015. Technical report, University of Texas at Austin"},{"issue":"1","key":"1589_CR30","doi-asserted-by":"publisher","first-page":"13006","DOI":"10.1038\/s41598-017-12961-9","volume":"7","author":"AG Reece","year":"2017","unstructured":"Reece AG, Reagan AJ, Lix KLM, Dodds PS, Danforth CM, Langer EJ (2017) Forecasting the onset and course of mental illness with twitter data. Sci Rep 7(1):13006. https:\/\/doi.org\/10.1038\/s41598-017-12961-9","journal-title":"Sci Rep"},{"issue":"7","key":"1589_CR31","doi-asserted-by":"publisher","first-page":"28754","DOI":"10.2196\/28754","volume":"9","author":"L Ren","year":"2021","unstructured":"Ren L, Lin H, Xu B, Zhang S, Yang L, Sun S (2021) Depression detection on reddit with an emotion-based attention network: Algorithm development and validation. JMIR Med Inform 9(7):28754. https:\/\/doi.org\/10.2196\/28754","journal-title":"JMIR Med Inform"},{"key":"1589_CR32","doi-asserted-by":"crossref","unstructured":"Sch\u00fctze H, Manning CD (2008) Raghavan P Introduction to Information Retrieval vol. 39. Cambridge University Press Cambridge","DOI":"10.1017\/CBO9780511809071"},{"issue":"20","key":"1589_CR33","doi-asserted-by":"publisher","first-page":"11274","DOI":"10.3390\/app152011274","volume":"15","author":"Z Shen","year":"2025","unstructured":"Shen Z, Paik I (2025) Temporal modeling of social media for depression forecasting: Deep learning approaches with pretrained embeddings. Appl Sci 15(20):11274","journal-title":"Appl Sci"},{"key":"1589_CR34","doi-asserted-by":"crossref","unstructured":"Shen G, Jia J, Nie L, Feng, F, Zhang C, Hu T, Chua T-S, Zhu W (2017) Depression detection via harvesting social media: A multimodal dictionary learning solution. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, pp. 3838\u20133844 . https:\/\/doi.org\/10.24963\/ijcai.2017\/536","DOI":"10.24963\/ijcai.2017\/536"},{"key":"1589_CR35","first-page":"106","volume":"8","author":"M Sykora","year":"2013","unstructured":"Sykora M, Jackson T, O\u2019Brien A, Elayan S (2013) Emotive ontology: extracting fine-grained emotions from terse, informal messages. IADIS Int J Comput Sci Inf Syst or IJCSIS 8:106\u2013118","journal-title":"IADIS Int J Comput Sci Inf Syst or IJCSIS"},{"key":"1589_CR36","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.knosys.2018.03.003","volume":"149","author":"ES Tellez","year":"2018","unstructured":"Tellez ES, Moctezuma D, Miranda-Jim\u00e9nez S, Graff M (2018) An automated text categorization framework based on hyperparameter optimization. Knowl-Based Syst 149:110\u2013123. https:\/\/doi.org\/10.1016\/j.knosys.2018.03.003","journal-title":"Knowl-Based Syst"},{"key":"1589_CR37","doi-asserted-by":"publisher","unstructured":"Tsugawa S, Kikuchi Y, Kishino F, Nakajima K, Ito Y, Ohsaki H (2015) Recognizing depression from twitter activity. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. CHI \u201915, pp. 3187\u20133196. Association for Computing Machinery, New York, NY, USA . https:\/\/doi.org\/10.1145\/2702123.2702280","DOI":"10.1145\/2702123.2702280"},{"key":"1589_CR38","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1016\/j.future.2021.05.032","volume":"124","author":"A-S Uban","year":"2021","unstructured":"Uban A-S, Chulvi B, Rosso P (2021) An emotion and cognitive based analysis of mental health disorders from social media data. Futur Gener Comput Syst 124:480\u2013494. https:\/\/doi.org\/10.1016\/j.future.2021.05.032","journal-title":"Futur Gener Comput Syst"},{"key":"1589_CR39","doi-asserted-by":"publisher","unstructured":"Villa-P\u00e9rez ME, Trejo LA, Moin MB, Stroulia E (2023) Extracting mental health indicators from english and spanish social media: A machine learning approach. IEEE Access 11, 128135\u2013128152 https:\/\/doi.org\/10.1109\/ACCESS.2023.3332289","DOI":"10.1109\/ACCESS.2023.3332289"},{"key":"1589_CR40","doi-asserted-by":"crossref","unstructured":"Wang W-Y, Tang Y-C, Du W-W, Peng W-C (2022) NYCU_TWD@LT-EDI-ACL2022: Ensemble models with VADER and contrastive learning for detecting signs of depression from social media. In: Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pp. 136\u2013139. Association for Computational Linguistics, Dublin, Ireland . https:\/\/doi.org\/10.18653\/v1\/2022.ltedi-1.15 . https:\/\/aclanthology.org\/2022.ltedi-1.15","DOI":"10.18653\/v1\/2022.ltedi-1.15"},{"key":"1589_CR41","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.inffus.2022.11.031","volume":"92","author":"T Zhang","year":"2023","unstructured":"Zhang T, Yang K, Ji S, Ananiadou S (2023) Emotion fusion for mental illness detection from social media: a survey. Info Fusion 92:231\u2013246. https:\/\/doi.org\/10.1016\/j.inffus.2022.11.031","journal-title":"Info Fusion"},{"key":"1589_CR42","doi-asserted-by":"crossref","unstructured":"Zhou S, Mohd M (2025) Mental health safety and depression detection in social media text data: A classification approach based on a deep learning model. IEEE Access","DOI":"10.1109\/ACCESS.2025.3559170"},{"issue":"1","key":"1589_CR43","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s11280-021-00992-2","volume":"25","author":"H Zogan","year":"2022","unstructured":"Zogan H, Razzak I, Wang X, Jameel S, Xu G (2022) Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media. World Wide Web 25(1):281\u2013304. https:\/\/doi.org\/10.1007\/s11280-021-00992-2","journal-title":"World Wide Web"}],"container-title":["Social Network Analysis and Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13278-026-01589-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-026-01589-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-026-01589-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T02:00:57Z","timestamp":1772589657000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13278-026-01589-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,25]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1589"],"URL":"https:\/\/doi.org\/10.1007\/s13278-026-01589-1","relation":{},"ISSN":["1869-5469"],"issn-type":[{"value":"1869-5469","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,25]]},"assertion":[{"value":"4 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no Conflict of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"44"}}