{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:22:39Z","timestamp":1780356159881,"version":"3.54.1"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T00:00:00Z","timestamp":1631145600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T00:00:00Z","timestamp":1631145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s11227-021-04040-8","type":"journal-article","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T07:03:19Z","timestamp":1631170999000},"page":"4709-4744","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":99,"title":["Automatic detection of depression symptoms in twitter using multimodal analysis"],"prefix":"10.1007","volume":"78","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1779-1019","authenticated-orcid":false,"given":"Ramin","family":"Safa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4063-4804","authenticated-orcid":false,"given":"Peyman","family":"Bayat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5785-3464","authenticated-orcid":false,"given":"Leila","family":"Moghtader","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,9]]},"reference":[{"issue":"4","key":"4040_CR1","doi-asserted-by":"publisher","first-page":"e0231924","DOI":"10.1371\/journal.pone.0231924","volume":"15","author":"J Gao","year":"2020","unstructured":"Gao J et al (2020) Mental health problems and social media exposure during COVID-19 outbreak. PLoS ONE 15(4):e0231924","journal-title":"PLoS ONE"},{"issue":"13","key":"4040_CR2","doi-asserted-by":"publisher","first-page":"4752","DOI":"10.3390\/ijerph17134752","volume":"17","author":"R Mart\u00ednez-Casta\u00f1o","year":"2020","unstructured":"Mart\u00ednez-Casta\u00f1o R, Pichel JC, Losada DE (2020) A big data platform for real time analysis of signs of depression in social media. Int J Environ Res Public Health 17(13):4752","journal-title":"Int J Environ Res Public Health"},{"key":"4040_CR3","doi-asserted-by":"crossref","unstructured":"R\u00edssola EA, Bahrainian SA, Crestani F. (2020) A Dataset for Research on Depression in Social Media. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, pp. 338\u2013342.","DOI":"10.1145\/3340631.3394879"},{"issue":"10159","key":"4040_CR4","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1016\/S0140-6736(18)32279-7","volume":"392","author":"SL James","year":"2018","unstructured":"James SL et al (2018) Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990\u20132017: a systematic analysis for the global burden of disease study 2017. The Lancet 392(10159):1789\u20131858","journal-title":"The Lancet"},{"key":"4040_CR5","unstructured":"Bloom DE et al., (2012) The global economic burden of noncommunicable diseases. Program on the Global Demography of Aging"},{"key":"4040_CR6","unstructured":"SAMHSA (2021) Major depressive episode in the past year among U.S. youths by gender 2004\u20132019. Statista - The Statistics Portal. https:\/\/www.statista.com\/statistics\/252323\/major-depressive-episode-among-us-youths-by-gender-since-2004\/ Accessed 12 August 2021"},{"key":"4040_CR7","doi-asserted-by":"crossref","unstructured":"Kang K, Yoon C, Kim EY (2016) Identifying depressive users in Twitter using multimodal analysis. In 2016 International Conference on Big Data and Smart Computing (BigComp), 2016: IEEE, pp. 231\u2013238.","DOI":"10.1109\/BIGCOMP.2016.7425918"},{"issue":"4","key":"4040_CR8","first-page":"573","volume":"8","author":"S Javadi","year":"2020","unstructured":"Javadi S, Safa R, Azizi M, Mirroshandel SA (2020) A Recommendation System for Finding Experts in Online Scientific Communities. J AI Data Min 8(4):573\u2013584","journal-title":"J AI Data Min"},{"key":"4040_CR9","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.copsyc.2016.01.004","volume":"9","author":"M Conway","year":"2016","unstructured":"Conway M, O\u2019Connor D (2016) Social media, big data, and mental health: current advances and ethical implications. Curr Opin Psychol 9:77\u201382","journal-title":"Curr Opin Psychol"},{"key":"4040_CR10","doi-asserted-by":"crossref","unstructured":"Ebert DD, Harrer M, Apolin\u00e1rio-Hagen J, Baumeister H, (2019) Digital interventions for mental disorders: key features, efficacy, and potential for artificial intelligence applications. In Frontiers in Psychiatry: Springer. 583\u2013627.","DOI":"10.1007\/978-981-32-9721-0_29"},{"key":"4040_CR11","doi-asserted-by":"crossref","unstructured":"Loveys K, Crutchley P, Wyatt E, Coppersmith G, (2017) Small but mighty: affective micropatterns for quantifying mental health from social media language. In Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology\u2014From Linguistic Signal to Clinical Reality, 2017, pp. 85\u201395.","DOI":"10.18653\/v1\/W17-3110"},{"key":"4040_CR12","doi-asserted-by":"publisher","first-page":"117822261879286","DOI":"10.1177\/1178222618792860","volume":"10","author":"G Coppersmith","year":"2018","unstructured":"Coppersmith G, Leary R, Crutchley P, Fine A (2018) Natural language processing of social media as screening for suicide risk. Biomed Inform Insights 10:1178222618792860","journal-title":"Biomed Inform Insights"},{"key":"4040_CR13","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1016\/j.future.2019.09.034","volume":"110","author":"FM Plaza-del-Arco","year":"2020","unstructured":"Plaza-del-Arco FM, Mart\u00edn-Valdivia MT, Ure\u00f1a-L\u00f3pez LA, Mitkov R (2020) Improved emotion recognition in Spanish social media through incorporation of lexical knowledge. Futur Gener Comput Syst 110:1000\u20131008","journal-title":"Futur Gener Comput Syst"},{"issue":"4","key":"4040_CR14","first-page":"240","volume":"80","author":"A Park","year":"2019","unstructured":"Park A, Bowling J, Shaw G, Li C, Chen S (2019) Adopting social media for improving health: opportunities and challenges. N C Med J 80(4):240\u2013243","journal-title":"N C Med J"},{"key":"4040_CR15","doi-asserted-by":"crossref","unstructured":"Coppersmith G, Dredze M, Harman C (2014) Quantifying mental health signals in Twitter. In Proceedings of the workshop on computational linguistics and clinical psychology: From linguistic signal to clinical reality, 2014, pp. 51\u201360.","DOI":"10.3115\/v1\/W14-3207"},{"key":"4040_CR16","doi-asserted-by":"crossref","unstructured":"De Choudhury M (2013) Role of social media in tackling challenges in mental health. In Proceedings of the 2nd international workshop on Socially-aware multimedia. pp. 49\u201352.","DOI":"10.1145\/2509916.2509921"},{"issue":"7","key":"4040_CR17","doi-asserted-by":"publisher","first-page":"e0198660","DOI":"10.1371\/journal.pone.0198660","volume":"13","author":"ZR Samani","year":"2018","unstructured":"Samani ZR, Guntuku SC, Moghaddam ME, Preo\u0163iuc-Pietro D, Ungar LH (2018) Cross-platform and cross-interaction study of user personality based on images on Twitter and Flickr. PLoS ONE 13(7):e0198660","journal-title":"PLoS ONE"},{"key":"4040_CR18","doi-asserted-by":"crossref","unstructured":"Schwartz HA et al. (2016) Predicting individual well-being through the language of social media. In Biocomputing 2016: Proceedings of the Pacific Symposium, 2016: World Scientific, pp. 516\u2013527.","DOI":"10.1142\/9789814749411_0047"},{"issue":"2","key":"4040_CR19","doi-asserted-by":"publisher","first-page":"e21","DOI":"10.2196\/mental.4822","volume":"3","author":"SR Braithwaite","year":"2016","unstructured":"Braithwaite SR, Giraud-Carrier C, West J, Barnes MD, Hanson CL (2016) Validating machine learning algorithms for Twitter data against established measures of suicidality\u201d. JMIR Mental Health 3(2):e21","journal-title":"JMIR Mental Health"},{"issue":"6","key":"4040_CR20","doi-asserted-by":"publisher","first-page":"e228","DOI":"10.2196\/jmir.7215","volume":"19","author":"A Wongkoblap","year":"2017","unstructured":"Wongkoblap A, Vadillo MA, Curcin V (2017) Researching mental health disorders in the era of social media: systematic review. J Med Internet Res 19(6):e228. https:\/\/doi.org\/10.2196\/jmir.7215","journal-title":"J Med Internet Res"},{"key":"4040_CR21","first-page":"28","volume-title":"International Conference of the Cross-Language Evaluation Forum for European Languages","author":"DE Losada","year":"2016","unstructured":"Losada DE, Crestani F (2016) A test collection for research on depression and language use. International Conference of the Cross-Language Evaluation Forum for European Languages. Springer, pp 28\u201339"},{"key":"4040_CR22","first-page":"296","volume-title":"European Conference on Information Retrieval","author":"EA R\u00edssola","year":"2020","unstructured":"R\u00edssola EA, Aliannejadi M, Crestani F (2020) Beyond Modelling: Understanding Mental Disorders in Online Social Media. European Conference on Information Retrieval. Springer, pp 296\u2013310"},{"key":"4040_CR23","first-page":"557","volume-title":"European Conference on Information Retrieval","author":"DE Losada","year":"2020","unstructured":"Losada DE, Crestani F, Parapar J (2020) eRisk 2020: Self-harm and Depression Challenges. European Conference on Information Retrieval. Springer, pp 557\u2013563"},{"issue":"2","key":"4040_CR24","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s12911-018-0632-8","volume":"18","author":"J Du","year":"2018","unstructured":"Du J et al (2018) Extracting psychiatric stressors for suicide from social media using deep learning. BMC Med Inform Decis Mak 18(2):43","journal-title":"BMC Med Inform Decis Mak"},{"key":"4040_CR25","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/978-3-319-59575-7_29","volume-title":"International Symposium on Bioinformatics Research and Applications","author":"L Ma","year":"2017","unstructured":"Ma L, Wang Z, Zhang Y (2017) Extracting depression symptoms from social networks and web blogs via text mining. International Symposium on Bioinformatics Research and Applications. Springer, pp 325\u2013330"},{"key":"4040_CR26","doi-asserted-by":"crossref","unstructured":"Chen X, Sykora 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":"4040_CR27","doi-asserted-by":"crossref","unstructured":"Wang T, Brede M, Ianni A, Mentzakis E (2017) Detecting and characterizing eating-disorder communities on social media. In Proceedings of the Tenth ACM International conference on web search and data mining. pp. 91\u2013100.","DOI":"10.1145\/3018661.3018706"},{"key":"4040_CR28","doi-asserted-by":"crossref","unstructured":"De Choudhury M, Counts S, Horvitz E (2013) Social media as a measurement tool of depression in populations. In Proceedings of the 5th Annual ACM Web Science Conference, 2013: ACM, pp. 47\u201356.","DOI":"10.1145\/2464464.2464480"},{"key":"4040_CR29","first-page":"1","volume":"13","author":"M De Choudhury","year":"2013","unstructured":"De Choudhury M, Gamon M, Counts S, Horvitz E (2013) Predicting depression via social media. Icwsm 13:1\u201310","journal-title":"Icwsm"},{"key":"4040_CR30","doi-asserted-by":"crossref","unstructured":"Preo\u0163iuc-Pietro D, Sap M, Schwartz HA, Ungar L (2015) Mental illness detection at the World Well-Being Project for the CLPsych 2015 shared task. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, 2015, pp. 40\u201345.","DOI":"10.3115\/v1\/W15-1205"},{"key":"4040_CR31","doi-asserted-by":"crossref","unstructured":"Burnap P, Colombo W, Scourfield J (2015) Machine classification and analysis of suicide-related communication on twitter. In Proceedings of the 26th ACM conference on hypertext & social media, 2015, pp. 75\u201384.","DOI":"10.1145\/2700171.2791023"},{"key":"4040_CR32","doi-asserted-by":"crossref","unstructured":"Tsugawa S, Kikuchi Y, Kishino F, Nakajima K, Itoh Y, Ohsaki H (2015) Recognizing depression from twitter activity\u201d, in Proceedings of the 33rd annual ACM conference on human factors in computing systems, 2015: ACM, pp. 3187\u20133196.","DOI":"10.1145\/2702123.2702280"},{"issue":"3","key":"4040_CR33","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1109\/TAFFC.2014.2315623","volume":"5","author":"T Nguyen","year":"2014","unstructured":"Nguyen T, Phung D, Dao B, Venkatesh S, Berk M (2014) Affective and content analysis of online depression communities. IEEE Trans Affect Comput 5(3):217\u2013226","journal-title":"IEEE Trans Affect Comput"},{"issue":"6","key":"4040_CR34","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1093\/jamia\/ocz009","volume":"26","author":"Z Yin","year":"2019","unstructured":"Yin Z, Sulieman LM, Malin BA (2019) A systematic literature review of machine learning in online personal health data. J Am Med Inform Assoc 26(6):561\u2013576","journal-title":"J Am Med Inform Assoc"},{"key":"4040_CR35","doi-asserted-by":"crossref","unstructured":"Hu Q, Li A, Heng F, Li J, Zhu T (2015) Predicting depression of social media user on different observation windows. In 2015 IEEE\/WIC\/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2015, vol. 1: IEEE, pp. 361\u2013364.","DOI":"10.1109\/WI-IAT.2015.166"},{"key":"4040_CR36","doi-asserted-by":"crossref","unstructured":"Coppersmith G, Ngo K, Leary R, Wood A (2016) \"Exploratory analysis of social media prior to a suicide attempt\u201d, in Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology, 2016, pp. 106\u2013117.","DOI":"10.18653\/v1\/W16-0311"},{"key":"4040_CR37","doi-asserted-by":"crossref","unstructured":"Huang X, Zhang L, Chiu D, Liu T, Li X, Zhu T (2014) Detecting suicidal ideation in Chinese microblogs with psychological lexicons. In 2014 IEEE 11th Intl Conf on Ubiquitous Intelligence and Computing and 2014 IEEE 11th Intl Conf on Autonomic and Trusted Computing and 2014 IEEE 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops, 2014: IEEE, pp. 844\u2013849.","DOI":"10.1109\/UIC-ATC-ScalCom.2014.48"},{"issue":"2","key":"4040_CR38","doi-asserted-by":"publisher","first-page":"e17","DOI":"10.2196\/mental.4227","volume":"2","author":"L Guan","year":"2015","unstructured":"Guan L, Hao B, Cheng Q, Yip PS, Zhu T (2015) Identifying Chinese microblog users with high suicide probability using internet-based profile and linguistic features: classification model. JMIR Mental Health. 2(2):e17","journal-title":"JMIR Mental Health."},{"key":"4040_CR39","doi-asserted-by":"crossref","unstructured":"Saravia E, Chang C-H, De Lorenzo RJ, Chen Y-S (2016) MIDAS: Mental illness detection and analysis via social media. In 2016 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2016: IEEE, pp. 1418\u20131421.","DOI":"10.1109\/ASONAM.2016.7752434"},{"key":"4040_CR40","unstructured":"Wang Y, Wang Z, Li C, Zhang Y, Wang H (2020) A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo. arXiv preprint arXiv:2008.11708"},{"key":"4040_CR41","doi-asserted-by":"crossref","unstructured":"Orabi AH, Buddhitha P, Orabi MH, Inkpen D (2018) Deep learning for depression detection of twitter users. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, 2018, pp. 88\u201397.","DOI":"10.18653\/v1\/W18-0609"},{"key":"4040_CR42","doi-asserted-by":"crossref","unstructured":"Coppersmith G, Harman C, Dredze M (2014) Measuring post traumatic stress disorder in Twitter. In Eighth international AAAI conference on weblogs and social media, 2014.","DOI":"10.1609\/icwsm.v8i1.14574"},{"key":"4040_CR43","unstructured":"Pennebaker JW, Boyd RL, Jordan K, Blackburn K (2015) The development and psychometric properties of LIWC2015"},{"key":"4040_CR44","doi-asserted-by":"crossref","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, pp. 1653\u20131660.","DOI":"10.1145\/3184558.3191624"},{"key":"4040_CR45","doi-asserted-by":"crossref","unstructured":"Wilson T et al. (2005) OpinionFinder: A system for subjectivity analysis. In Proceedings of HLT\/EMNLP 2005 Interactive Demonstrations","DOI":"10.3115\/1225733.1225751"},{"issue":"12","key":"4040_CR46","doi-asserted-by":"publisher","first-page":"2544","DOI":"10.1002\/asi.21416","volume":"61","author":"M Thelwall","year":"2010","unstructured":"Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) Sentiment strength detection in short informal text. J Am Soc Inform Sci Technol 61(12):2544\u20132558","journal-title":"J Am Soc Inform Sci Technol"},{"key":"4040_CR47","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.techfore.2015.06.035","volume":"99","author":"AO Durahim","year":"2015","unstructured":"Durahim AO, Co\u015fkun M (2015) # iamhappybecause: Gross National Happiness through Twitter analysis and big data. Technol Forecast Soc Chang 99:92\u2013105","journal-title":"Technol Forecast Soc Chang"},{"issue":"3","key":"4040_CR48","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1162\/artl_a_00034","volume":"17","author":"J Bollen","year":"2011","unstructured":"Bollen J, Gon\u00e7alves B, Ruan G, Mao H (2011) Happiness is assortative in online social networks. Artif Life 17(3):237\u2013251","journal-title":"Artif Life"},{"key":"4040_CR49","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993\u20131022","journal-title":"J Mach Learn Res"},{"key":"4040_CR50","unstructured":"Paul MJ, Dredze M (2011) You are what you tweet: Analyzing twitter for public health. In Fifth international AAAI conference on weblogs and social media: Citeseer."},{"key":"4040_CR51","first-page":"1","volume":"2018","author":"S Ji","year":"2018","unstructured":"Ji S, Yu CP, Fung S-F, Pan S, Long G (2018) Supervised learning for suicidal ideation detection in online user content. Complexity 2018:1\u201310","journal-title":"Complexity"},{"issue":"17","key":"4040_CR52","doi-asserted-by":"publisher","first-page":"24103","DOI":"10.1007\/s11042-019-7390-1","volume":"78","author":"A Kumar","year":"2019","unstructured":"Kumar A, Garg G (2019) Sentiment analysis of multimodal twitter data. Multimed Tools Appl 78(17):24103\u201324119","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"4040_CR53","first-page":"1","volume":"56","author":"CY Chiu","year":"2020","unstructured":"Chiu CY, Lane HY, Koh JL, Chen AL (2020) Multimodal depression detection on instagram considering time interval of posts. J Intell Inf Syst 56(1):1\u201323","journal-title":"J Intell Inf Syst"},{"issue":"1","key":"4040_CR54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1140\/epjds\/s13688-016-0097-x","volume":"6","author":"AG Reece","year":"2017","unstructured":"Reece AG, Danforth CM (2017) Instagram photos reveal predictive markers of depression. EPJ Data Sci 6(1):1\u201312","journal-title":"EPJ Data Sci"},{"key":"4040_CR55","doi-asserted-by":"crossref","unstructured":"Guntuku SC, Preotiuc-Pietro D, Eichstaedt JC, Ungar LH (2019) What twitter profile and posted images reveal about depression and anxiety. In Proceedings of the International AAAI Conference on Web and Social Media. 13, 236\u2013246.","DOI":"10.1609\/icwsm.v13i01.3225"},{"key":"4040_CR56","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations."},{"issue":"S2","key":"4040_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.51983\/ajcst-2019.8.S2.2037","volume":"8","author":"V Bonta","year":"2019","unstructured":"Bonta V, Janardhan NKN (2019) A comprehensive study on lexicon based approaches for sentiment analysis. Asian J Comput Sci Technol 8(S2):1\u20136","journal-title":"Asian J Comput Sci Technol"},{"key":"4040_CR58","unstructured":"Association AP (2013) Diagnostic and statistical manual of mental disorders (DSM-5\u00ae). American Psychiatric Pub."},{"issue":"3","key":"4040_CR59","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\u2013emotion association lexicon. Comput Intell 29(3):436\u2013465","journal-title":"Comput Intell"},{"key":"4040_CR60","doi-asserted-by":"publisher","first-page":"113142","DOI":"10.1016\/j.dss.2019.113142","volume":"127","author":"CEH Chua","year":"2019","unstructured":"Chua CEH, Storey VC, Li X, Kaul M (2019) Developing insights from social media using semantic lexical chains to mine short text structures. Decis Support Syst 127:113142","journal-title":"Decis Support Syst"},{"issue":"1","key":"4040_CR61","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1002\/aris.1440380105","volume":"38","author":"ST Dumais","year":"2004","unstructured":"Dumais ST (2004) Latent semantic analysis. Ann Rev Inf Sci Technol 38(1):188\u2013230","journal-title":"Ann Rev Inf Sci Technol"},{"key":"4040_CR62","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"4040_CR63","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1016\/j.future.2017.05.030","volume":"86","author":"K Mao","year":"2018","unstructured":"Mao K, Niu J, Chen H, Wang L, Atiquzzaman M (2018) Mining of marital distress from microblogging social networks: A case study on Sina Weibo. Futur Gener Comput Syst 86:1481\u20131490","journal-title":"Futur Gener Comput Syst"},{"key":"4040_CR64","doi-asserted-by":"publisher","unstructured":"Zhou J, Zogan H, Yang S, Jameel S, Xu G, Chen F (2021) Detecting community depression dynamics due to covid-19 pandemic in australia. IEEE Transactions on Computational Social Systems. https:\/\/doi.org\/10.1109\/TCSS.2020.3047604","DOI":"10.1109\/TCSS.2020.3047604"},{"key":"4040_CR65","doi-asserted-by":"publisher","unstructured":"Kim J, Lee J, Park E, Han J (2020) A deep learning model for detecting mental illness from user content on\nsocial media. Sci Rep 10(1):1\u20136. https:\/\/doi.org\/10.1038\/s41598-020-68764-y","DOI":"10.1038\/s41598-020-68764-y"},{"key":"4040_CR66","doi-asserted-by":"crossref","unstructured":"Guntuku SC, Preotiuc-Pietro D, Eichstaedt JC, Ungar LH (2019) What twitter profile and posted images reveal about depression and anxiety. In: Proceedings of the International AAAI Conference on Web and Social Media, vol 13, pp 236\u2013246","DOI":"10.1609\/icwsm.v13i01.3225"},{"key":"4040_CR67","doi-asserted-by":"publisher","unstructured":"Tadesse MM, Lin H, Xu B, Yang L (2019) Detection of depression-related posts in reddit social media forum. IEEE Access 7:44883\u201344893. https:\/\/doi.org\/10.1109\/ACCESS.2019.2909180","DOI":"10.1109\/ACCESS.2019.2909180"},{"key":"4040_CR68","doi-asserted-by":"publisher","unstructured":"Islam MR, Kabir MA, Ahmed A, Kamal AR, Wang H, Ulhaq A (2018) Depression detection from social\nnetwork data using machine learning techniques. Health Inf Sci Syst 6(1):1\u20132. https:\/\/doi.org\/10.1007\/s13755-018-0046-0","DOI":"10.1007\/s13755-018-0046-0"},{"key":"4040_CR69","unstructured":"Ferwerda B, Tkalcic M (2018) You are what you post: What the content of Instagram pictures tells about\nusers\u2019 personality. In: The 23rd International on Intelligent User Interfaces, March 7\u201311, Tokyo, Japan. CEUR-WS"},{"key":"4040_CR70","unstructured":"Chen X, Sykora M, Jackson T, Elayan S, Munir F. Tweeting your mental health: an exploration of different classifiers and features with emotional signals in identifying mental health conditions"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-04040-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-021-04040-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-04040-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T09:37:01Z","timestamp":1744191421000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-021-04040-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,9]]},"references-count":70,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["4040"],"URL":"https:\/\/doi.org\/10.1007\/s11227-021-04040-8","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,9]]},"assertion":[{"value":"19 August 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}