{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T13:04:10Z","timestamp":1772456650356,"version":"3.50.1"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":54,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"the European University of Atlantic"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-01123-9","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T07:53:58Z","timestamp":1767772438000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model"],"prefix":"10.1007","volume":"19","author":[{"given":"Erol","family":"KINA","sequence":"first","affiliation":[]},{"given":"Jin-Ghoo","family":"Choi","sequence":"additional","affiliation":[]},{"given":"Abid","family":"Ishaq","sequence":"additional","affiliation":[]},{"given":"Rahman","family":"Shafique","sequence":"additional","affiliation":[]},{"given":"Monica Gracia","family":"Villar","sequence":"additional","affiliation":[]},{"given":"Eduardo Silva","family":"Alvarado","sequence":"additional","affiliation":[]},{"given":"Isabel de la Torre","family":"Diez","sequence":"additional","affiliation":[]},{"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"1123_CR1","unstructured":"WHO, R.: WHO. Suicide, https:\/\/www.who.int\/health-topics\/suicide#tab=tab_1 [Accessed: (22july2023)] (2022)"},{"key":"1123_CR2","doi-asserted-by":"publisher","first-page":"540","DOI":"10.3389\/fpsyt.2018.00540","volume":"9","author":"J Bilsen","year":"2018","unstructured":"Bilsen, J.: Suicide and youth: risk factors. Front. Psych. 9, 540 (2018)","journal-title":"Front. Psych."},{"issue":"3","key":"1123_CR3","doi-asserted-by":"publisher","first-page":"760","DOI":"10.3390\/ijerph9030760","volume":"9","author":"P V\u00e4rnik","year":"2012","unstructured":"V\u00e4rnik, P.: Suicide in the world. Int. J. Environ. Res. Public Health 9(3), 760\u2013771 (2012)","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"1123_CR4","doi-asserted-by":"publisher","DOI":"10.1002\/9780470698976","volume-title":"The International Handbook of Suicide and Attempted Suicide","author":"K Hawton","year":"2000","unstructured":"Hawton, K., Van Heeringen, K.: The International Handbook of Suicide and Attempted Suicide. Wiley, New York (2000)"},{"key":"1123_CR5","unstructured":"World Health Organization: Preventing suicide: a global imperative. https:\/\/www.who.int\/publications\/i\/item\/9789241564779 (2014)"},{"issue":"1","key":"1123_CR6","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/S2215-0366(14)70222-6","volume":"1","author":"RC O\u2019Connor","year":"2014","unstructured":"O\u2019Connor, R.C., Nock, M.K.: The psychology of suicidal behaviour. The Lancet Psychiatry 1(1), 73\u201385 (2014)","journal-title":"The Lancet Psychiatry"},{"key":"1123_CR7","unstructured":"American Foundation for Suicide Prevention: Risk Factors, Protective Factors, and Warning Signs. American Foundation for Suicide Prevention. https:\/\/afsp.org\/risk-factors-protective-factors-and-warning-signs\/ note=Accessed on 23 April, 2025 (2022)"},{"issue":"2","key":"1123_CR8","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1037\/bul0000084","volume":"143","author":"JC Franklin","year":"2017","unstructured":"Franklin, J.C., Ribeiro, J.D., Fox, K.R., Bentley, K.H., Kleiman, E.M., Huang, X., Musacchio, K.M., Jaroszewski, A.C., Chang, B.P., Nock, M.K.: Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychol. Bull. 143(2), 187 (2017)","journal-title":"Psychol. Bull."},{"issue":"12","key":"1123_CR9","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s10916-020-01669-5","volume":"44","author":"G Castillo-S\u00e1nchez","year":"2020","unstructured":"Castillo-S\u00e1nchez, G., Marques, G., Dorronzoro, E., Rivera-Romero, O., Franco-Mart\u00edn, M., Torre-D\u00edez, I.: Suicide risk assessment using machine learning and social networks: a scoping review. J. Med. Syst. 44(12), 205 (2020)","journal-title":"J. Med. Syst."},{"issue":"6","key":"1123_CR10","doi-asserted-by":"publisher","first-page":"9840","DOI":"10.2196\/jmir.9840","volume":"20","author":"AE Alada\u011f","year":"2018","unstructured":"Alada\u011f, A.E., Muderrisoglu, S., Akbas, N.B., Zahmacioglu, O., Bingol, H.O.: Detecting suicidal ideation on forums: proof-of-concept study. J. Med. Internet Res. 20(6), 9840 (2018)","journal-title":"J. Med. Internet Res."},{"key":"1123_CR11","unstructured":"Harmer, B., Lee, S., Rizvi, A., Saadabadi, A.: Suicidal Ideation. StatPearls Publishing, Treasure Island (FL), ??? (2025). http:\/\/europepmc.org\/books\/NBK565877"},{"issue":"3","key":"1123_CR12","first-page":"13","volume":"13","author":"RI Simon","year":"2014","unstructured":"Simon, R.I.: Passive suicidal ideation: still a high-risk clinical scenario. Current Psychiatry 13(3), 13\u201315 (2014)","journal-title":"Current Psychiatry"},{"issue":"3","key":"1123_CR13","first-page":"553","volume":"101","author":"AN Weber","year":"2017","unstructured":"Weber, A.N., Michail, M., Thompson, A., Fiedorowicz, J.G.: Psychiatric emergencies: assessing and managing suicidal ideation. Med. Clin. 101(3), 553\u2013571 (2017)","journal-title":"Med. Clin."},{"issue":"5","key":"1123_CR14","doi-asserted-by":"publisher","first-page":"0250448","DOI":"10.1371\/journal.pone.0250448","volume":"16","author":"M Gaur","year":"2021","unstructured":"Gaur, M., Aribandi, V., Alambo, A., Kursuncu, U., Thirunarayan, K., Beich, J., Pathak, J., Sheth, A.: Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS. PLoS ONE 16(5), 0250448 (2021)","journal-title":"PLoS ONE"},{"issue":"1","key":"1123_CR15","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1176\/appi.prcp.20190015","volume":"2","author":"JM Twenge","year":"2020","unstructured":"Twenge, J.M.: Increases in depression, self-harm, and suicide among us adolescents after 2012 and links to technology use: possible mechanisms. Psychiatr. Res. Clin. Pract. 2(1), 19\u201325 (2020)","journal-title":"Psychiatr. Res. Clin. Pract."},{"key":"1123_CR16","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.chb.2015.12.066","volume":"58","author":"T Green","year":"2016","unstructured":"Green, T., Wilhelmsen, T., Wilmots, E., Dodd, B., Quinn, S.: Social anxiety, attributes of online communication and self-disclosure across private and public Facebook communication. Comput. Hum. Behav. 58, 206\u2013213 (2016)","journal-title":"Comput. Hum. Behav."},{"key":"1123_CR17","doi-asserted-by":"crossref","unstructured":"Abboute, A., Boudjeriou, Y., Entringer, G., Az\u00e9, J., Bringay, S., Poncelet, P.: Mining twitter for suicide prevention. In: Natural Language Processing and Information Systems: 19th International Conference on Applications of Natural Language to Information Systems, NLDB 2014, Montpellier, France, June 18-20, 2014. Proceedings 19, pp. 250\u2013253 (2014). Springer","DOI":"10.1007\/978-3-319-07983-7_36"},{"key":"1123_CR18","doi-asserted-by":"crossref","unstructured":"Chiang, W.-C., Cheng, P.-H., Su, M.-J., Chen, H.-S., Wu, S.-W., Lin, J.-K.: Socio-health with personal mental health records: suicidal-tendency observation system on Facebook for Taiwanese adolescents and young adults. In: 2011 IEEE 13th International Conference on e-Health Networking, Applications and Services, pp. 46\u201351 (2011). IEEE","DOI":"10.1109\/HEALTH.2011.6026784"},{"key":"1123_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.111655","volume":"159","author":"SU Amin","year":"2025","unstructured":"Amin, S.U., Jung, Y., Fayaz, M., Kim, B., Seo, S.: Enhancing pine wilt disease detection with synthetic data and external attention-based transformers. Eng. Appl. Artif. Intell. 159, 111655 (2025)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"1123_CR20","unstructured":"Amin, S.U., Abbas, M.S., Kim, B., Jung, Y., Seo, S.: Enhanced anomaly detection in pandemic surveillance videos: an attention approach with EfficientNet-B0 and CBAM integration. IEEE Access (2024)"},{"key":"1123_CR21","doi-asserted-by":"crossref","unstructured":"Ul Amin, S., Kim, Y., Sami, I., Park, S., Seo, S.: An efficient attention-based strategy for anomaly detection in surveillance video. Comput. Syst. Sci. Eng. 46(3) (2023)","DOI":"10.32604\/csse.2023.034805"},{"issue":"7","key":"1123_CR22","doi-asserted-by":"publisher","first-page":"2300706","DOI":"10.1002\/aisy.202300706","volume":"6","author":"S Ul Amin","year":"2024","unstructured":"Ul Amin, S., Kim, B., Jung, Y., Seo, S., Park, S.: Video anomaly detection utilizing efficient spatiotemporal feature fusion with 3d convolutions and long short-term memory modules. Adv. Intell. Syst. 6(7), 2300706 (2024)","journal-title":"Adv. Intell. Syst."},{"issue":"2","key":"1123_CR23","first-page":"46","volume":"6","author":"MES Abdelmalak","year":"2023","unstructured":"Abdelmalak, M.E.S., Gaber, K.S., Ahmed, M.A., OubeBlika, N., Zaki, A.M., Eid, M.M.: BER-XGBoost: pothole detection based on feature extraction and optimized XGBoost using BER metaheuristic algorithm. J. Artif. Intell. Metaheur. 6(2), 46\u201355 (2023)","journal-title":"J. Artif. Intell. Metaheur."},{"key":"1123_CR24","doi-asserted-by":"crossref","unstructured":"Jabbar, A., Liaqat, H.B., Akram, A., Sana, M.U., Azp\u00edroz, I.D., Diez, I.D.L.T., Ashraf, I.: A lesion-based diabetic retinopathy detection through hybrid deep learning model. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3373467"},{"issue":"19","key":"1123_CR25","doi-asserted-by":"publisher","first-page":"4351","DOI":"10.3390\/s19194351","volume":"19","author":"I Ashraf","year":"2019","unstructured":"Ashraf, I., Hur, S., Park, Y.: Indoor positioning on disparate commercial smartphones using Wi-Fi access points coverage area. Sensors 19(19), 4351 (2019)","journal-title":"Sensors"},{"issue":"8","key":"1123_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3605889","volume":"22","author":"M Mujahid","year":"2023","unstructured":"Mujahid, M., Kanwal, K., Rustam, F., Aljedaani, W., Ashraf, I.: Arabic ChatGPT tweets classification using RoBERTa and BERT ensemble model. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 22(8), 1\u201323 (2023)","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inf. Process."},{"issue":"1","key":"1123_CR27","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/bioengineering10010018","volume":"10","author":"H ZainEldin","year":"2022","unstructured":"ZainEldin, H., Gamel, S.A., El-Kenawy, E.-S.M., Alharbi, A.H., Khafaga, D.S., Ibrahim, A., Talaat, F.M.: Brain tumor detection and classification using deep learning and sine-cosine fitness grey wolf optimization. Bioengineering 10(1), 18 (2022)","journal-title":"Bioengineering"},{"issue":"10","key":"1123_CR28","doi-asserted-by":"publisher","first-page":"9564","DOI":"10.1016\/j.jksuci.2021.11.010","volume":"34","author":"S Renjith","year":"2022","unstructured":"Renjith, S., Abraham, A., Jyothi, S.B., Chandran, L., Thomson, J.: An ensemble deep learning technique for detecting suicidal ideation from posts in social media platforms. J. King Saud Univ. Comput. Inf. Sci. 34(10), 9564\u20139575 (2022)","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"issue":"2S","key":"1123_CR29","first-page":"3217","volume":"10","author":"J Deepa","year":"2023","unstructured":"Deepa, J., Shriraaman, S., Shruti, V., Vasanth, G.: Detecting and determining degree of suicidal ideation on tweets using LSTM and machine learning models. J. Surv. Fish. Sci. 10(2S), 3217\u20133224 (2023)","journal-title":"J. Surv. Fish. Sci."},{"issue":"2","key":"1123_CR30","volume":"2","author":"M Chatterjee","year":"2022","unstructured":"Chatterjee, M., Kumar, P., Samanta, P., Sarkar, D.: Suicide ideation detection from online social media: a multi-modal feature based technique. Int. J. Inf. Manag. Data Insights 2(2), 100103 (2022)","journal-title":"Int. J. Inf. Manag. Data Insights"},{"key":"1123_CR31","unstructured":"Agarwal, D.B. K: Deep learning based approach for detecting suicidal ideation in Hindi\u2013English code-mixed text: Baseline and corpus. In: Proceedings of the 18th International Conference on Natural Language Processing (ICON), pp. 100\u2013105 (2021)"},{"issue":"03","key":"1123_CR32","first-page":"1258","volume":"13","author":"N Bandari","year":"2022","unstructured":"Bandari, N., Kancharla, M., Kunsoth, U.: Suicidal tweets detection in online social media using machine learning. Turk. J. Comput. Math. Educ. (TURCOMAT) 13(03), 1258\u20131267 (2022)","journal-title":"Turk. J. Comput. Math. Educ. (TURCOMAT)"},{"issue":"1","key":"1123_CR33","doi-asserted-by":"publisher","first-page":"6157249","DOI":"10.1155\/2018\/6157249","volume":"2018","author":"S Ji","year":"2018","unstructured":"Ji, S., Yu, C.P., Fung, S.-F., Pan, S., Long, G.: Supervised learning for suicidal ideation detection in online user content. Complexity 2018(1), 6157249 (2018)","journal-title":"Complexity"},{"issue":"1","key":"1123_CR34","doi-asserted-by":"publisher","first-page":"146045822198939","DOI":"10.1177\/1460458221989395","volume":"27","author":"N Nordin","year":"2021","unstructured":"Nordin, N., Zainol, Z., Mohd Noor, M.H., Lai Fong, C.: A comparative study of machine learning techniques for suicide attempts predictive model. Health Inform. J. 27(1), 1460458221989395 (2021)","journal-title":"Health Inform. J."},{"issue":"13","key":"1123_CR35","doi-asserted-by":"publisher","first-page":"8197","DOI":"10.3390\/ijerph19138197","volume":"19","author":"J Liu","year":"2022","unstructured":"Liu, J., Shi, M., Jiang, H.: Detecting suicidal ideation in social media: an ensemble method based on feature fusion. Int. J. Environ. Res. Public Health 19(13), 8197 (2022)","journal-title":"Int. J. Environ. Res. Public Health"},{"issue":"1","key":"1123_CR36","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3390\/a13010007","volume":"13","author":"MM Tadesse","year":"2019","unstructured":"Tadesse, M.M., Lin, H., Xu, B., Yang, L.: Detection of suicide ideation in social media forums using deep learning. Algorithms 13(1), 7 (2019)","journal-title":"Algorithms"},{"key":"1123_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.110708","volume":"144","author":"V Gupta","year":"2021","unstructured":"Gupta, V., Jain, N., Katariya, P., Kumar, A., Mohan, S., Ahmadian, A., Ferrara, M.: An emotion care model using multimodal textual analysis on covid-19. Chaos Solit. Fractals 144, 110708 (2021)","journal-title":"Chaos Solit. Fractals"},{"key":"1123_CR38","doi-asserted-by":"crossref","unstructured":"Sharma, D., Gupta, V., Singh, V.K.: Detection of homophobia & transphobia in Malayalam and Tamil: exploring deep learning methods. In: Advanced Network Technologies and Intelligent Computing, pp. 217\u2013226. Springer, Cham (2023)","DOI":"10.1007\/978-3-031-28183-9_15"},{"issue":"1","key":"1123_CR39","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s13278-024-01334-6","volume":"14","author":"S Arora","year":"2024","unstructured":"Arora, S., Agrawal, V., Kumar, D., Arora, S., Banshal, S.K.: Sentimental impact of fake news on social media using an integrated ensemble framework. Soc. Netw. Anal. Min. 14(1), 185 (2024)","journal-title":"Soc. Netw. Anal. Min."},{"issue":"2","key":"1123_CR40","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s13748-024-00326-z","volume":"13","author":"M Qorich","year":"2024","unstructured":"Qorich, M., El Ouazzani, R.: Advanced deep learning and large language models for suicide ideation detection on social media. Prog. Artif. Intell 13(2), 135\u2013147 (2024)","journal-title":"Prog. Artif. Intell"},{"key":"1123_CR41","doi-asserted-by":"publisher","first-page":"28025","DOI":"10.1109\/ACCESS.2024.3366653","volume":"12","author":"A Pourkeyvan","year":"2024","unstructured":"Pourkeyvan, A., Safa, R., Sorourkhah, A.: Harnessing the power of hugging face transformers for predicting mental health disorders in social networks. IEEE Access 12, 28025\u201328035 (2024)","journal-title":"IEEE Access"},{"key":"1123_CR42","doi-asserted-by":"publisher","first-page":"1401322","DOI":"10.3389\/fpubh.2024.1401322","volume":"12","author":"A Li","year":"2024","unstructured":"Li, A.: Predicting negative attitudes towards suicide in social media texts: prediction model development and validation study. Front. Public Health 12, 1401322 (2024)","journal-title":"Front. Public Health"},{"key":"1123_CR43","doi-asserted-by":"publisher","first-page":"49927","DOI":"10.2196\/49927","volume":"26","author":"Z Kaminsky","year":"2024","unstructured":"Kaminsky, Z., McQuaid, R.J., Hellemans, K.G., Patterson, Z.R., Saad, M., Gabrys, R.L., Kendzerska, T., Abizaid, A., Robillard, R.: Machine learning-based suicide risk prediction model for suicidal trajectory on social media following suicidal mentions: Independent algorithm validation. J. Med. Internet Res. 26, 49927 (2024)","journal-title":"J. Med. Internet Res."},{"key":"1123_CR44","unstructured":"Komati, N.: Suicide and depression detection"},{"key":"1123_CR45","doi-asserted-by":"publisher","first-page":"9375","DOI":"10.1007\/s12652-020-02654-z","volume":"12","author":"A Ishaq","year":"2021","unstructured":"Ishaq, A., Umer, M., Mushtaq, M.F., Medaglia, C., Siddiqui, H.U.R., Mehmood, A., Choi, G.S.: Extensive hotel reviews classification using long short term memory. J. Ambient. Intell. Human. Comput. 12, 9375\u20139385 (2021)","journal-title":"J. Ambient. Intell. Human. Comput."},{"key":"1123_CR46","doi-asserted-by":"publisher","first-page":"30234","DOI":"10.1109\/ACCESS.2020.2972632","volume":"8","author":"F Rustam","year":"2020","unstructured":"Rustam, F., Mehmood, A., Ahmad, M., Ullah, S., Khan, D.M., Choi, G.S.: Classification of Shopify app user reviews using novel multi text features. IEEE Access 8, 30234\u201330244 (2020)","journal-title":"IEEE Access"},{"key":"1123_CR47","doi-asserted-by":"crossref","unstructured":"Qader, W.A., Ameen, M.M., Ahmed, B.I.: An overview of bag of words: importance, implementation, applications, and challenges. In: 2019 International Engineering Conference (IEC), pp. 200\u2013204 (2019). IEEE","DOI":"10.1109\/IEC47844.2019.8950616"},{"issue":"3","key":"1123_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1361684.1361686","volume":"26","author":"HC Wu","year":"2008","unstructured":"Wu, H.C., Luk, R.W.P., Wong, K.F., Kwok, K.L.: Interpreting TF-IDF term weights as making relevance decisions. ACM Trans. Inf. Syst. (TOIS) 26(3), 1\u201337 (2008)","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"1123_CR49","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"issue":"2","key":"1123_CR50","doi-asserted-by":"publisher","first-page":"0245909","DOI":"10.1371\/journal.pone.0245909","volume":"16","author":"F Rustam","year":"2021","unstructured":"Rustam, F., Khalid, M., Aslam, W., Rupapara, V., Mehmood, A., Choi, G.S.: A performance comparison of supervised machine learning models for covid-19 tweets sentiment analysis. PLoS ONE 16(2), 0245909 (2021)","journal-title":"PLoS ONE"},{"key":"1123_CR51","doi-asserted-by":"publisher","DOI":"10.1002\/9781118548387","volume-title":"Applied Logistic Regression","author":"DW Hosmer Jr","year":"2013","unstructured":"Hosmer, D.W., Jr., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression, vol. 398. Wiley, New York (2013)"},{"issue":"8","key":"1123_CR52","doi-asserted-by":"publisher","first-page":"4012","DOI":"10.1109\/TIP.2018.2834830","volume":"27","author":"A Paul","year":"2018","unstructured":"Paul, A., Mukherjee, D.P., Das, P., Gangopadhyay, A., Chintha, A.R., Kundu, S.: Improved random forest for classification. IEEE Trans. Image Process. 27(8), 4012\u20134024 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"1123_CR53","doi-asserted-by":"publisher","first-page":"712","DOI":"10.7717\/peerj-cs.712","volume":"7","author":"B Gaye","year":"2021","unstructured":"Gaye, B., Zhang, D., Wulamu, A.: Sentiment classification for employees reviews using regression vector-stochastic gradient descent classifier (RV-SGDC). Peer J. Comput. Sci. 7, 712 (2021)","journal-title":"Peer J. Comput. Sci."},{"issue":"1","key":"1123_CR54","first-page":"1558","volume":"18","author":"AJ Wyner","year":"2017","unstructured":"Wyner, A.J., Olson, M., Bleich, J., Mease, D.: Explaining the success of adaboost and random forests as interpolating classifiers. J. Mach. Learn. Res. 18(1), 1558\u20131590 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"1123_CR55","doi-asserted-by":"crossref","unstructured":"Jahromi, A.H., Taheri, M.: A non-parametric mixture of gaussian naive bayes classifiers based on local independent features. In: 2017 Artificial Intelligence and Signal Processing Conference (AISP), pp. 209\u2013212 (2017). IEEE","DOI":"10.1109\/AISP.2017.8324083"},{"issue":"1","key":"1123_CR56","first-page":"1144","volume":"36","author":"VY Kulkarni","year":"2013","unstructured":"Kulkarni, V.Y., Sinha, P.K.: Random forest classifiers: a survey and future research directions. Int. J. Adv. Comput 36(1), 1144\u20131153 (2013)","journal-title":"Int. J. Adv. Comput"},{"issue":"14","key":"1123_CR57","first-page":"5947","volume":"10","author":"LM Gladence","year":"2015","unstructured":"Gladence, L.M., Karthi, M., Anu, V.M.: A statistical comparison of logistic regression and different bayes classification methods for machine learning. ARPN J. Eng. Appl. Sci. 10(14), 5947\u20135953 (2015)","journal-title":"ARPN J. Eng. Appl. Sci."},{"key":"1123_CR58","doi-asserted-by":"crossref","unstructured":"Lee, E., Rustam, F., Shahzad, H.F., Washington, P.B., Ishaq, A., Ashraf, I.: Drug usage safety from drug reviews with hybrid machine learning approach. Comput. Syst. Sci. Eng. 46(1) (2023)","DOI":"10.32604\/csse.2023.029059"},{"key":"1123_CR59","unstructured":"Yin, W., Kann, K., Yu, M., Sch\u00fctze, H.: Comparative study of CNN and RNN for natural language processing. arXiv:1702.01923 (2017)"},{"key":"1123_CR60","doi-asserted-by":"publisher","first-page":"128359","DOI":"10.1109\/ACCESS.2021.3112546","volume":"9","author":"M Manzoor","year":"2021","unstructured":"Manzoor, M., Umer, M., Sadiq, S., Ishaq, A., Ullah, S., Madni, H.A., Bisogni, C.: Rfcnn: traffic accident severity prediction based on decision level fusion of machine and deep learning model. IEEE Access 9, 128359\u2013128371 (2021)","journal-title":"IEEE Access"},{"key":"1123_CR61","doi-asserted-by":"crossref","unstructured":"Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 324\u2013328 (2016). IEEE","DOI":"10.1109\/YAC.2016.7804912"},{"issue":"1","key":"1123_CR62","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/s44196-024-00491-y","volume":"17","author":"R Vasant Bidwe","year":"2024","unstructured":"Vasant Bidwe, R., Mishra, S., Kamini Bajaj, S., Kotecha, K.: Attention-focused eye gaze analysis to predict autistic traits using transfer learning. Int. J. Comput. Intell. Syst. 17(1), 120 (2024)","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"1123_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.mex.2025.103166","volume":"14","author":"R Bidwe","year":"2025","unstructured":"Bidwe, R., Mishra, S., Bajaj, S., Kotecha, K.: Leveraging hybrid model of convnextbase and lightgbm for early asd detection via eye-gaze analysis. MethodsX 14, 103166 (2025)","journal-title":"MethodsX"},{"key":"1123_CR64","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.7717\/peerj-cs.1141","volume":"8","author":"N Aslam","year":"2022","unstructured":"Aslam, N., Xia, K., Rustam, F., Lee, E., Ashraf, I.: Self voting classification model for online meeting app review sentiment analysis and topic modeling. Peer J. Comput. Sci. 8, 1141 (2022)","journal-title":"Peer J. Comput. Sci."},{"key":"1123_CR65","doi-asserted-by":"publisher","unstructured":"Garg, T., Gupta, S.K.: A hybrid stacked ensemble technique to improve classification accuracy for neurological disorder detection on reddit posts. In: 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 256\u2013260 (2022). https:\/\/doi.org\/10.1109\/CICN56167.2022.10008283","DOI":"10.1109\/CICN56167.2022.10008283"},{"key":"1123_CR66","doi-asserted-by":"crossref","unstructured":"Reshi, A.A., Rustam, F., Aljedaani, W., Shafi, S., Alhossan, A., Alrabiah, Z., Ahmad, A., Alsuwailem, H., Almangour, T.A., Alshammari, M.A., et al.: Covid-19 vaccination-related sentiments analysis: a case study using worldwide twitter dataset. In: Healthcare, vol. 10, p. 411 (2022). MDPI","DOI":"10.3390\/healthcare10030411"},{"issue":"15","key":"1123_CR67","doi-asserted-by":"publisher","first-page":"2259036","DOI":"10.1142\/S0218001422590364","volume":"36","author":"S Raman","year":"2022","unstructured":"Raman, S., Gupta, V., Nagrath, P., Santosh, K.: Hate and aggression analysis in NLP with explainable AI. Int. J. Pattern Recognit. Artif. Intell. 36(15), 2259036 (2022)","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"1123_CR68","doi-asserted-by":"crossref","unstructured":"Sharma, D., Gupta, V., Singh, V.K., Chakravarthi, B.R.: Stop the hate, spread the hope: An ensemble model for hope speech detection in english and dravidian languages. ACM Trans. Asian Low-Resour. Lang. Inf. Process. (2025)","DOI":"10.1145\/3716383"},{"issue":"20","key":"1123_CR69","doi-asserted-by":"publisher","first-page":"3980","DOI":"10.3390\/electronics13203980","volume":"13","author":"BG Bokolo","year":"2024","unstructured":"Bokolo, B.G., Liu, Q.: Advanced comparative analysis of machine learning and transformer models for depression and suicide detection in social media texts. Electronics 13(20), 3980 (2024)","journal-title":"Electronics"},{"key":"1123_CR70","doi-asserted-by":"crossref","unstructured":"Kulkarni, S.S., Hareesh, B.V.N., Enduri, M.K.: Explainable depression detection in social media using transformer-based models: a comparative analysis of machine learning. In: 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 321\u2013325 (2024). IEEE","DOI":"10.1109\/CICN63059.2024.10847487"},{"issue":"19","key":"1123_CR71","doi-asserted-by":"publisher","first-page":"12635","DOI":"10.3390\/ijerph191912635","volume":"19","author":"TH Aldhyani","year":"2022","unstructured":"Aldhyani, T.H., Alsubari, S.N., Alshebami, A.S., Alkahtani, H., Ahmed, Z.A.: Detecting and analyzing suicidal ideation on social media using deep learning and machine learning models. Int. J. Environ. Res. Public Health 19(19), 12635 (2022)","journal-title":"Int. J. Environ. Res. Public Health"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-01123-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-01123-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-01123-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T12:15:28Z","timestamp":1772453728000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-01123-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,7]]},"references-count":71,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1123"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-01123-9","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,7]]},"assertion":[{"value":"27 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 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":"Ethics Approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"100"}}