{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:52:50Z","timestamp":1743047570997,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819784868"},{"type":"electronic","value":"9789819784875"}],"license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"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\/978-981-97-8487-5_25","type":"book-chapter","created":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T07:03:22Z","timestamp":1730617402000},"page":"353-366","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cascade Large Language Model via In-Context Learning for Depression Detection on Chinese Social Media"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2988-1897","authenticated-orcid":false,"given":"Tong","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanrong","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richang","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"issue":"10","key":"25_CR1","doi-asserted-by":"publisher","first-page":"2446","DOI":"10.1166\/jmihi.2020.3169","volume":"10","author":"H Ahmad","year":"2020","unstructured":"Ahmad, H., Asghar, M.Z., Alotaibi, F.M., Hameed, I.A.: Applying deep learning technique for depression classification in social media text. J. Med. Imaging Health Inf. 10(10), 2446\u20132451 (2020)","journal-title":"J. Med. Imaging Health Inf."},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"American Psychiatric\u00a0Association, D., Association, A.P., et\u00a0al.: Diagnostic and Statistical Manual of Mental Disorders: DSM-5, vol.\u00a05. American psychiatric association Washington, DC (2013)","DOI":"10.1176\/appi.books.9780890425596"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Chen, D., Mei, J.P., Zhang, H., Wang, C., Feng, Y., Chen, C.: Knowledge distillation with the reused teacher classifier. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11933\u201311942 (2022)","DOI":"10.1109\/CVPR52688.2022.01163"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"25_CR5","unstructured":"Chen, Z., Yang, X., Lin, J., Sun, C., Huang, J., Chang, K.C.C.: Cascade speculative drafting for even faster llm inference (2023). arXiv:2312.11462"},{"key":"25_CR6","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling (2014). arXiv:1412.3555"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Danner, M., Hadzic, B., Gerhardt, S., Ludwig, S., Uslu, I., Shao, P., Weber, T., Shiban, Y., Ratsch, M.: Advancing mental health diagnostics: Gpt-based method for depression detection. In: 2023 62nd Annual Conference of the Society of Instrument and Control Engineers (SICE), pp. 1290\u20131296. IEEE (2023)","DOI":"10.23919\/SICE59929.2023.10354236"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Deng, B., Wang, Z., Shu, X., Shu, J.: Transformer-based graphic-text fusion depressive tendency detection. In: 2023 6th International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 701\u2013705. IEEE (2023)","DOI":"10.1109\/ICAIBD57115.2023.10206166"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Deng, T., Shu, X., Shu, J.: A depression tendency detection model fusing weibo content and user behavior. In: 2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 304\u2013309. IEEE (2022)","DOI":"10.1109\/ICAIBD55127.2022.9820478"},{"key":"25_CR10","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding (2018). arXiv:1810.04805"},{"key":"25_CR11","unstructured":"Dong, Q., Li, L., Dai, D., Zheng, C., Wu, Z., Chang, B., Sun, X., Xu, J., Sui, Z.: A survey for in-context learning (2022). arXiv:2301.00234"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Guo, Y., Liu, J., Wang, L., Qin, W., Hao, S., Hong, R.: A prompt-based topic-modeling method for depression detection on low-resource data. IEEE Trans. Comput. Soc. Syst. (2023)","DOI":"10.1109\/TCSS.2023.3260080"},{"issue":"7","key":"25_CR13","doi-asserted-by":"publisher","first-page":"3815","DOI":"10.1002\/int.22704","volume":"37","author":"L He","year":"2022","unstructured":"He, L., Guo, C., Tiwari, P., Su, R., Pandey, H.M., Dang, W.: Depnet: An automated industrial intelligent system using deep learning for video-based depression analysis. Int. J. Intell. Syst. 37(7), 3815\u20133835 (2022)","journal-title":"Int. J. Intell. Syst."},{"issue":"8","key":"25_CR14","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Kang, Y., Jiang, X., Yin, Y., Shang, Y., Zhou, X.: Deep transformation learning for depression diagnosis from facial images. In: Biometric Recognition: 12th Chinese Conference, CCBR 2017, Shenzhen, China, October 28-29, 2017, Proceedings 12, pp. 13\u201322. Springer (2017)","DOI":"10.1007\/978-3-319-69923-3_2"},{"key":"25_CR16","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.Y.: Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"25_CR17","unstructured":"Kim, Y., Xu, X., McDuff, D., Breazeal, C., Park, H.W.: Health-llm: Large language models for health prediction via wearable sensor data (2024). arXiv:2401.06866"},{"issue":"1\u20133","key":"25_CR18","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.jad.2008.06.026","volume":"114","author":"K Kroenke","year":"2009","unstructured":"Kroenke, K., Strine, T.W., Spitzer, R.L., Williams, J.B., Berry, J.T., Mokdad, A.H.: The phq-8 as a measure of current depression in the general population. J. Affect. Disord. 114(1\u20133), 163\u2013173 (2009)","journal-title":"J. Affect. Disord."},{"key":"25_CR19","unstructured":"Lai, T., Shi, Y., Du, Z., Wu, J., Fu, K., Dou, Y., Wang, Z.: Psy-llm: Scaling up global mental health psychological services with ai-based large language models (2023). arXiv:2307.11991"},{"key":"25_CR20","unstructured":"Liu, J.M., Li, D., Cao, H., Ren, T., Liao, Z., Wu, J.: Chatcounselor: A large language models for mental health support (2023). arXiv:2309.15461"},{"key":"25_CR21","unstructured":"Liu, W., Lei, F., Luo, T., Lei, J., He, S., Zhao, J., Liu, K.: Mmhqa-icl: Multimodal in-context learning for hybrid question answering over text, tables and images (2023). arXiv:2309.04790"},{"issue":"11","key":"25_CR22","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1016\/S2215-0366(21)00251-0","volume":"8","author":"J Lu","year":"2021","unstructured":"Lu, J., Xu, X., Huang, Y., Li, T., Ma, C., Xu, G., Yin, H., Xu, X., Ma, Y., Wang, L., et al.: Prevalence of depressive disorders and treatment in china: a cross-sectional epidemiological study. Lancet Psychiatry 8(11), 981\u2013990 (2021)","journal-title":"Lancet Psychiatry"},{"key":"25_CR23","doi-asserted-by":"crossref","unstructured":"Ma, Y., Cao, Y., Hong, Y., Sun, A.: Large language model is not a good few-shot information extractor, but a good reranker for hard samples! (2023). arXiv:2303.08559","DOI":"10.18653\/v1\/2023.findings-emnlp.710"},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Malhotra, A., Jindal, R.: Deep learning techniques for suicide and depression detection from online social media: a scoping review. Appl. Soft Comput. 109713 (2022)","DOI":"10.1016\/j.asoc.2022.109713"},{"key":"25_CR25","unstructured":"Qin, W., Chen, Z., Wang, L., Lan, Y., Ren, W., Hong, R.: Read, diagnose and chat: Towards explainable and interactive llms-augmented depression detection in social media (2023). arXiv:2305.05138"},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Sadeghi, M., Egger, B., Agahi, R., Richer, R., Capito, K., Rupp, L.H., Schindler-Gmelch, L., Berking, M., Eskofier, B.M.: Exploring the capabilities of a language model-only approach for depression detection in text data. In: 2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), pp.\u00a01\u20135. IEEE (2023)","DOI":"10.1109\/BHI58575.2023.10313367"},{"key":"25_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116076","volume":"189","author":"S Sardari","year":"2022","unstructured":"Sardari, S., Nakisa, B., Rastgoo, M.N., Eklund, P.: Audio based depression detection using convolutional autoencoder. Expert Syst. Appl. 189, 116076 (2022)","journal-title":"Expert Syst. Appl."},{"key":"25_CR28","doi-asserted-by":"crossref","unstructured":"Tao, Y., Yang, M., Shen, H., Yang, Z., Weng, Z., Hu, B.: Classifying anxiety and depression through llms virtual interactions: A case study with chatgpt. In: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2259\u20132264. IEEE (2023)","DOI":"10.1109\/BIBM58861.2023.10385305"},{"key":"25_CR29","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1007\/s13246-020-00938-4","volume":"43","author":"PP Thoduparambil","year":"2020","unstructured":"Thoduparambil, P.P., Dominic, A., Varghese, S.M.: Eeg-based deep learning model for the automatic detection of clinical depression. Phys. Eng. Sci. Med. 43, 1349\u20131360 (2020)","journal-title":"Phys. Eng. Sci. Med."},{"key":"25_CR30","doi-asserted-by":"crossref","unstructured":"Turcan, E., McKeown, K.: Dreaddit: A reddit dataset for stress analysis in social media (2019). arXiv:1911.00133","DOI":"10.18653\/v1\/D19-6213"},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, Z., Li, C., Zhang, Y., Wang, H.: A multimodal feature fusion-based method for individual depression detection on sina weibo. In: 2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC), pp.\u00a01\u20138. IEEE (2020)","DOI":"10.1109\/IPCCC50635.2020.9391501"},{"key":"25_CR32","doi-asserted-by":"crossref","unstructured":"Wang, Z., Deng, B., Shu, X., Shu, J.: Multimodal depression detection model fusing emotion knowledge graph. In: 2023 6th International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 21\u201326. IEEE (2023)","DOI":"10.1109\/ICAIBD57115.2023.10206303"},{"key":"25_CR33","unstructured":"Xu, X., Yao, B., Dong, Y., Yu, H., Hendler, J., Dey, A.K., Wang, D.: Leveraging large language models for mental health prediction via online text data (2023). arXiv:2307.14385"},{"key":"25_CR34","doi-asserted-by":"crossref","unstructured":"Yan, J., Shu, X., Shu, J.: Depressive emotion tendency detection for users on social platform based on fusion of graph and text. In: 2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 337\u2013341. IEEE (2022)","DOI":"10.1109\/ICAIBD55127.2022.9820498"},{"key":"25_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Long, X., Shu, X., Shu, J.: Depression tendency detection of weibo users based on knowledge graph. In: 2023 6th International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 542\u2013547. IEEE (2023)","DOI":"10.1109\/ICAIBD57115.2023.10206350"},{"key":"25_CR36","unstructured":"Zhao, W.X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et\u00a0al.: A survey of large language models (2023). arXiv:2303.18223"},{"key":"25_CR37","doi-asserted-by":"crossref","unstructured":"Zhao, W., Liu, Y., Wan, Y., Wang, Y., Wu, Q., Deng, Z., Du, J., Liu, S., Xu, Y., Yu, P.S.: Knn-icl: Compositional task-oriented parsing generalization with nearest neighbor in-context learning (2023). arXiv:2312.10771","DOI":"10.18653\/v1\/2024.naacl-long.19"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8487-5_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T07:08:53Z","timestamp":1730617733000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8487-5_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,4]]},"ISBN":["9789819784868","9789819784875"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8487-5_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,4]]},"assertion":[{"value":"4 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}