{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T21:59:30Z","timestamp":1742939970553,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031813382"},{"type":"electronic","value":"9783031813399"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-81339-9_8","type":"book-chapter","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T17:45:33Z","timestamp":1739295933000},"page":"80-93","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Revolutionizing Suicide Ideation Detection in Social Media: An Ensemble Optimized Bi-GRU with Attention Approach"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3501-0500","authenticated-orcid":false,"given":"Shiv Shankar Prasad","family":"Shukla","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4121-4959","authenticated-orcid":false,"given":"Maheshwari Prasad","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,12]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Katchapakirin, K., Wongpatikaseree, K., Yomaboot, P., Kaewpitakkun, Y.: Facebook social media for depression detection in the Thai community. In: 2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 1\u20136. IEEE","DOI":"10.1109\/JCSSE.2018.8457362"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Gao, J., Cheng, Q., Yu, P.L.: Detecting comments showing risk for suicide in YouTube. In: Arai, K., Bhatia, R., Kapoor, S. (eds.) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol. 880, pp. 385\u2013400. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-02686-8_30","DOI":"10.1007\/978-3-030-02686-8_30"},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Valeriano, K., Condori-Larico, A., Sulla-Torres, J.: Detection of suicidal intent in Spanish language social networks using machine learning. Int. J. Adv. Comput. Sci. Appl. 11(4) (2020)","DOI":"10.14569\/IJACSA.2020.0110489"},{"issue":"6","key":"8_CR4","doi-asserted-by":"publisher","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), e9840 (2018)","journal-title":"J. Med. Internet Res."},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Sawhney, R., Manchanda, P., Mathur, P., Shah, R., Singh, R.: Exploring and learning suicidal ideation connotations on social media with deep learning. In: Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 167\u2013175 (2018)","DOI":"10.18653\/v1\/W18-6223"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Zhao, X., Lin, S., Huang, Z.: Text Classification of Micro-blog's\u201d Tree Hole\u201d Based on Convolutional Neural Network. In: Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence, pp. 1\u20135 (2018)","DOI":"10.1145\/3302425.3302501"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Shing, H.C., Nair, S., Zirikly, A., Friedenberg, M., Daum\u00e9 III, H., Resnik, P.: Expert, crowdsourced, and machine assessment of suicide risk via online postings. In: Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pp. 25\u201336 (2018)","DOI":"10.18653\/v1\/W18-0603"},{"key":"8_CR8","doi-asserted-by":"crossref","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)","DOI":"10.1155\/2018\/6157249"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Hevia, A.G., Men\u00e9ndez, R.C., Gayo-Avello, D.: Analyzing the use of existing systems for the clpsych 2019 shared task. In: Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pp. 148\u2013151 (2019)","DOI":"10.18653\/v1\/W19-3017"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Morales, M., Dey, P., Theisen, T., Belitz, D., Chernova, N.:. An investigation of deep learning systems for suicide risk assessment. In: Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pp. 177\u2013181 (2019)","DOI":"10.18653\/v1\/W19-3023"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Matero, M., et al.: Suicide risk assessment with multi-level dual-context language and BERT. In: Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pp. 39\u201344 (2019)","DOI":"10.18653\/v1\/W19-3005"},{"issue":"1","key":"8_CR12","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":"8_CR13","unstructured":"Wang, N., et al.:. Learning models for suicide prediction from social media posts.\u00a0arXiv preprint arXiv:2105.03315 (2021)"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Sawhney, R., Joshi, H., Shah, R., Flek, L.: Suicide ideation detection via social and temporal user representations using hyperbolic learning. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2176\u20132190 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.176"},{"issue":"4","key":"8_CR15","doi-asserted-by":"publisher","first-page":"442","DOI":"10.3390\/e24040442","volume":"24","author":"Z Li","year":"2022","unstructured":"Li, Z., Zhou, J., An, Z., Cheng, W., Hu, B.: Deep hierarchical ensemble model for suicide detection on imbalanced social media data. Entropy 24(4), 442 (2022)","journal-title":"Entropy"},{"issue":"13","key":"8_CR16","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":"8_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-022-00589-7","volume":"5","author":"T Zhang","year":"2022","unstructured":"Zhang, T., Schoene, A.M., Ji, S., Ananiadou, S.: Natural language processing applied to mental illness detection: a narrative review. NPJ Digit. Med. 5(1), 1\u201313 (2022)","journal-title":"NPJ Digit. Med."},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8) (1997)","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"8_CR19","doi-asserted-by":"crossref","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. (2021)","DOI":"10.1016\/j.jksuci.2021.11.010"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Haque, F., Nur, R. U., Al Jahan, S., Mahmud, Z., Shah, F.M.: A transformer based approach to detect suicidal ideation using pre-trained language models. In: 2020 23rd International Conference on Computer and Information Technology (ICCIT), pp. 1\u20135. IEEE (2020)","DOI":"10.1109\/ICCIT51783.2020.9392692"},{"key":"8_CR21","unstructured":"Abdulsalam, A., Alhothali, A.: Suicidal Ideation Detection on Social Media: A Review of Machine Learning Methods. arXiv preprint arXiv:2201.10515 (2022)"},{"key":"8_CR22","unstructured":"Huang, X., Li, X., Liu, T., Chiu, D., Zhu, T., Zhang, L.:. Topic model for identifying suicidal ideation in Chinese microblog. In: Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation, pp. 553\u2013562 (2015)"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Cao, L., et al.: Latent suicide risk detection on microblog via suicide-oriented word embeddings and layered Attention.\u00a0arXiv preprint arXiv:1910.12038 (2019)","DOI":"10.18653\/v1\/D19-1181"},{"issue":"2","key":"8_CR24","first-page":"77","volume":"18","author":"J Du","year":"2018","unstructured":"Du, J., et al.: Extracting psychiatric stressors for suicide from social media using deep learning. BMC Med. Inform. Decis. Mak. 18(2), 77\u201387 (2018)","journal-title":"BMC Med. Inform. Decis. Mak."},{"issue":"6","key":"8_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3422824","volume":"53","author":"R Skaik","year":"2020","unstructured":"Skaik, R., Inkpen, D.: Using social media for mental health surveillance: a review. ACM Comput. Surv. (CSUR) 53(6), 1\u201331 (2020)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"12","key":"8_CR26","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1038\/s41562-017-0234-y","volume":"1","author":"MA Just","year":"2017","unstructured":"Just, M.A., et al.: Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth. Nat. Hum. Behav. 1(12), 911\u2013919 (2017)","journal-title":"Nat. Hum. Behav."},{"issue":"5","key":"8_CR27","doi-asserted-by":"publisher","first-page":"216","DOI":"10.9740\/mhc.2015.09.216","volume":"5","author":"M Lotito","year":"2015","unstructured":"Lotito, M., Cook, E.: A review of suicide risk assessment instruments and approaches. Mental Health Clin. 5(5), 216\u2013223 (2015)","journal-title":"Mental Health Clin."},{"issue":"12","key":"8_CR28","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.8729","volume":"19","author":"Z Tan","year":"2017","unstructured":"Tan, Z., Liu, X., Liu, X., Cheng, Q., Zhu, T.: Designing microblog direct messages to engage social media users with suicide ideation: interview and survey study on Weibo. J. Med. Internet Res. 19(12), e8729 (2017)","journal-title":"J. Med. Internet Res."},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Varathan, K.D., Talib, N.: Suicide detection system based on Twitter. In: 2014 Science and Information Conference, pp. 785\u2013788. IEEE (2014)","DOI":"10.1109\/SAI.2014.6918275"},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Gunn, J.F., Lester, D.: Twitter postings and suicide: an analysis of the postings of a fatal suicide in the 24 hours prior to death. Suicidologi, 17(3) (2012)","DOI":"10.5617\/suicidologi.2173"},{"key":"8_CR31","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.comcom.2015.07.018","volume":"73","author":"GB Colombo","year":"2016","unstructured":"Colombo, G.B., Burnap, P., Hodorog, A., Scourfield, J.: Analyzing the connectivity and communication of suicidal users on Twitter. Comput. Commun. 73, 291\u2013300 (2016)","journal-title":"Comput. Commun."},{"issue":"2","key":"8_CR32","doi-asserted-by":"publisher","DOI":"10.2196\/mental.4822","volume":"3","author":"SR Braithwaite","year":"2016","unstructured":"Braithwaite, S.R., Giraud-Carrier, C., West, J., Barnes, M.D., Hanson, C.L.: Validating machine learning algorithms for Twitter data against established measures of suicidality. JMIR Mental Health 3(2), e4822 (2016)","journal-title":"JMIR Mental Health"},{"issue":"3","key":"8_CR33","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1177\/2167702617691560","volume":"5","author":"CG Walsh","year":"2017","unstructured":"Walsh, C.G., Ribeiro, J.D., Franklin, J.C.: Predicting risk of suicide attempts over time through machine learning. Clin. Psychol. Sci. 5(3), 457\u2013469 (2017)","journal-title":"Clin. Psychol. Sci."},{"key":"8_CR34","unstructured":"Kaggale. https:\/\/www.kaggle.com\/datasets\/nikhileswarkomati\/suicide-watch"},{"key":"8_CR35","unstructured":"Github. https:\/\/github.com\/laxmimerit\/twitter-suicidal-intention-dataset"},{"key":"8_CR36","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1007\/s00354-022-00191-1","volume":"40","author":"A Chadha","year":"2022","unstructured":"Chadha, A., Kaushik, B.: A hybrid deep learning model using grid search and cross-validation for effective classification and prediction of suicidal ideation from social network data. New Gener. Comput. 40, 889\u2013914 (2022). https:\/\/doi.org\/10.1007\/s00354-022-00191-1","journal-title":"New Gener. Comput."},{"key":"8_CR37","doi-asserted-by":"publisher","unstructured":"Ghosal, S., Jain, A.: Depression and suicide risk detection on social media using fastText embedding and XGBoost classifier. Procedia Comput. Sci. 218, 1631\u20131639. https:\/\/doi.org\/10.1016\/j.procs.2023.01.141, ISSN 1877-0509","DOI":"10.1016\/j.procs.2023.01.141"}],"container-title":["Communications in Computer and Information Science","Computational Intelligence in Communications and Business Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-81339-9_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T17:45:52Z","timestamp":1739295952000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-81339-9_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031813382","9783031813399"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-81339-9_8","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICBA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Intelligence in Communications and Business Analytics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Patna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 January 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 January 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicba2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cicba.in","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}