[1] 共青团中央维护青少年权益部, 中国互联网络信息中心. (2024). 第5次全国未成年人互联网使用情况调查报告 . 2025-06-24 取自https://www.cnnic.net.cn/NMediaFile/2023/1225/MAIN1703484375296SPBHV29S0V.pdf
[2] 美国心理学会. (2013). 2013年美国压力状况™:信息图表. 2025-06-05 取自https://www.apa.org/news/press/releases/stress/2013/infographics
[3] 千瓜数据. (2024). 2024「活跃用户」研究报告(小红书平台). 2025-06-05 取自https://www.qian-gua.com/blog/detail/2898.html
[4] 世界卫生组织. (2024). 青少年精神卫生. 2025-06-05 取自https://www.who.int/zh/news-room/fact-sheets/detail/adolescent-mental-health
[5] 肖锐, 刘明义, 涂志莹, 王忠杰. (2022). 基于社交媒体文本挖掘的个人事件检测方法. 计算机应用, 42(11), 3513-3519. doi: 10.11772/j.issn.1001-9081.2022010106
[6] 新浪微博数据中心. (2021). 微博2020用户发展报告. 2025-06-05 取自https://data.weibo.com/report/reportDetail?id=456
[7] 新浪微博数据中心. (2024). 2023微博年轻用户发展报告. 2025-06-05 取自https://data.weibo.com/report/reportDetail?id=471
[8] 中国互联网络信息中心. (2023). 《第5次全国未成年人互联网使用情况调查报告》发布. 2025-06-05 取自https://www.cnnic.net.cn/n4/2023/1225/c116-10908.html
[9] 中国互联网络信息中心. (2024). 第53次《中国互联网络发展状况统计报告》发布. 2025-06-05 取自 https://www.cnnic.net.cn/n4/2024/0321/c208-10962.html
[10] 中国科学院心理研究所. (2022). 中国国民心理健康发展报告(2021-2022). 北京: 社会科学文献出版社.
[11] Anshul A., Pranav G. S., Rehman M. Z. U., & Kumar N. (2024). A multimodal framework for depression detection during COVID-19 via harvesting social media.IEEE T Comput Soc Sy, 11(2), 2872-2888. doi: 10.1109/TCSS.2023.3309229.
[12] Beniwal, R., & Saraswat, P. (2024). A hybrid BERT-CNN approach for depression detection on social media using multimodal data.Comput J, 67(7), 2453-2472. doi: 10.1093/comjnl/bxae018
[13] Borah, T., & Ganesh Kumar, S. (2023). Application of NLP and machine learning for mental health improvement. In Gupta, D., Khanna, A., Hassanien, A. E., Anand, S. , & Jaiswal A. (Eds.), International Conference on Innovative Computing and Communications (Vol. 492, pp.219-228). Singapore: Springer Nature. doi:10.1007/978-981-19-3679-1_16
[14] Buddhitha, P., & Inkpen, D. (2023). Multi-task learning to detect suicide ideation and mental disorders among social media users.Front Res Metr Anal, 8, 1152535. doi: 10.3389/frma.2023.1152535
[15] Cao L., Zhang H., Feng L., Wei Z., Wang X., Li N., & He X. (2019, November). Latent suicide risk detection on microblog via suicide-oriented word embeddings and layered attention. International joint conference on natural language processing & Conference on empirical methods in natural language processing, Hong Kong, China. doi: 10.48550/arxiv.1910.12038
[16] Cao L., Zhang H. J., & Feng L. (2020). Building and using personal knowledge graph to improve suicidal ideation detection on social media.IEEE T Multimedia, 24, 87-102. doi: 10.1109/TMM.2020. 3046867
[17] Cao L., Zhang H. J., Li N. Y., Wang X., Ri W., & Feng L. (2022). Category-aware chronic stress detection on microblogs. IEEE J Biomed Health Inform, 26(2), 852-864. doi: 10.1109/JBHI.2021.3090467
[18] Chadha, A., & Kaushik, B. (2022). 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(4), 889-914. doi: 10.1007/s00354-022-00191-1
[19] Crosswell, A. D., & Lockwood, K. G. (2020). Best practices for stress measurement: How to measure psychological stress in health research.Health Psychol Open, 7(2), 2055102920933072. doi: 10.1177/2055102920933072
[20] Dumka A., Verma P., Singh R., Kumar Bisht A., Anand D., Moaiteq Aljahdali H., .. Aparicio Obregon S. (2022). A novel deep learning based healthcare model for COVID-19 pandemic stress analysis.Comput Mater Con, 72(3), 6029-6044. doi: 10.32604/cmc.2022.024698
[21] Feng Z. N., Li N. Y., Feng L., Chen D. Y., & Zhu C. H. (2021). Leveraging ECG signals and social media for stress detection.Behav Inform Technol, 40(2), 116-133. doi: 10.1080/0144929x.2019.1673820
[22] Huh-Yoo J., Razi A., Nguyen D. N., Regmi S., & Wisniewski P. J. (2023, April). “Help Me:” examining youth’s private pleas for support and the responses received from peers via instagram direct messages. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, Hamburg, Germany. doi: 10.1145/3544548.3581233
[23] Hussain S., Ayoub M., Yang Y., Abdul Wahid J., Khan A., Moller D. P. F., .. Hou W. Y. (2023). Ensemble deep learning framework for situational aspects-based annotation and classification of international student’s tweets during COVID-19.Comput Mater Con, 75(3), 5355-5377. doi: 10.32604/cmc.2023.036779
[24] Ilias L., Mouzakitis S., Askounis D. (2024). Calibration of transformer-based models for identifying stress and depression in social media.IEEE T Comput Soc Sy, 11(2), 1979-1990.
[25] Ji, Y. (2024). Replacement or complement: A niche analysis of Weibo, Xiaohongshu, and TikTok.Res Sq. doi: 10.21203/rs.3.rs-4280661/v1
[26] Kodati, D., & Tene, R. (2023). Identifying suicidal emotions on social media through transformer-based deep learning.Appl Intell, 53(10), 11885-11917. doi: 10.1007/s10489-022-04060-8
[27] Li N. Y., Zhang H. J., & Feng L. (2023). Incorporating forthcoming events and personality traits in social media based stress prediction.IEEE T Affect Comput, 14(1), 603-621. doi: 10.1109/TAFFC.2021.3076294
[28] Li Q., Zhao L., Xue Y., Jin L., & Feng L. (2017, October). Exploring the impact of co-experiencing stressor events for teens stress forecasting. In International Conference on Web Information Systems Engineering, Puschino, Russia.
[29] Li Q., Zhao L., Xue Y. Y., Feng L. (2021). Stress-buffering pattern of positive events on adolescents: An exploratory study based on social networks.Comput Hum Behav, 114, 106565. doi: 10.1016/j.chb.2020.106565
[30] Li Y. P., Feng Z. N., & Feng L. (2015). Using candlestick charts to predict adolescent stress trend on micro-blog.Procedia Comput Sci, 63, 221-228. doi: 10.1016/j.procs.2015.08.337
[31] Li Y. P., Huang J., Wang H., & Feng L. (2015, June). Predicting teenager’s future stress level from micro-blog. 2015 IEEE 28th International Symposium on Computer-Based Medical Systems, Sao Carlos, Brazil. doi: 10.1109/CBMS.2015.25
[32] Lin H., Jia J., Nie L., Shen G., & Chua T. S. (2016, July). What does social media say about your stress?. In International Joint Conference on Artificial Intelligence, New York, USA.
[33] Lin H. J., Jia J., Qiu J. Z., Zhang Y. F., Shen G. Y., Xie L. X., .. Chua T. S. (2017). Detecting stress based on social interactions in social networks.IEEE T Knowl Data En, 29(9), 1820-1833. doi: 10.1109/TKDE.2017.2686382
[34] Lin H. J., Jia J., Guo Q., Xue Y. Y., Huang J., Cai L. H., .. Feng L. (2014, July). Psychological stress detection from cross-media microblog data using deep sparse neural network. IN 2014 IEEE International Conference on Multimedia and Expo (ICME), Chengdu, China. doi: 10.1109/ICME.2014.6890213
[35] Luo X. H., Ma J. W., & Hu Y. Q. (2025). A dynamic bidirectional system of stress processes: Feedback loops between stressors, psychological distress, and physical symptoms.Health Psychol, 44(2), 154-165. doi: 10.1037/hea0001414
[36] Nabi R. L., Prestin A., & So J. (2013). Facebook friends with (health) benefits? Exploring social network site use and perceptions of social support, stress, and well-being.Cyberpsychol Behav Soc Netw, 16(10), 721-727. doi: 10.1089/cyber.2012.0521
[37] National Cancer Institute. (2022). Stress and Cancer. Retrieved June 5, 2025, from https://www.cancer.gov/about-cancer/coping/feelings/stress-fact-sheet
[38] Nijhawan T., Attigeri G., & Ananthakrishna T. (2022). Stress detection using natural language processing and machine learning over social interactions. J Big Data, 9(1), 33. doi: 10.1186/s40537-022-00575-6
[39] Podber, N., & Gruenewald, T. L. (2023). Positive life experiences and mortality: Examination of psychobiological pathways. Soc Sci Med, 335, 116192. doi: 10.1016/j.socscimed.2023.116192
[40] Ringwald W. R., Nielsen S. R., Mostajabi J., Vize C. E., van den Berg T., Manuck S. B., .. Wright, A. G. C. (2024). Characterizing stress processes by linking big five personality states, traits, and day-to-day stressors. J Res Pers, 110, 104487. doi: 10.1016/j.jrp.2024.104487
[41] Ullah W., Oliveira-Silva P., Nawaz M., Zulqarnain R. M., Siddique I., & Sallah M. (2025). Identification of depressing tweets using natural language processing and machine learning: Application of grey relational grades.J Radiat Res Appl Sc, 18(1), 101299. doi: 10.1016/j.jrras.2025.101299
[42] Wang X., Cao L., Zhang H., Feng L., Ding Y., & Li N. (2022, April). A meta-learning based stress category detection framework on social media. Proceedings of the ACM Web Conference 2022, Lyon, France. doi: 10.1145/3485447.3512013
[43] Wang X., Feng L., Zhang H., Cao L., Zeng K., Li Q.,.. Clifton D. (2025, April). MISE: Meta-knowledge inheritance for social media-based stressor estimation. Proceedings of the ACM on Web Conference 2025, Sydney, Australia. doi: 10.1145/3696410.3714901
[44] Wang X., Zhang H. J., Cao L., & Feng L. (2020, October). Leverage Social media for personalized stress detection. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, USA. doi: 10.1145/3394171.3413596
[45] Wang X., Zhang H., Cao L., Zeng K., Li Q., Li N., & Feng L. (2023, August). Contrastive learning of stress-specific word embedding for social media based stress detection. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, USA. doi: 10.1145/3580305.3599795
[46] Xue Y. Y., Li Q., & Feng L. (2019). Teensensor: Gaussian processes for micro-blog based teen’s acute and chronic stress detection.Comput Syst Sci Eng, 34(3), 151-164. doi: 10.32604/csse.2019.34.151
[47] Xue Y. Y., Li Q., Jin L., Feng L., Clifton D. A., & Clifford, G. D. (2014). Detecting adolescent psychological pressures from micro-blog. In Health Information Science. Springer International Publishing (pp. 83-94). doi: 10.1007/978-3-319-06269-3_10
[48] Zhang H. J., Cao L., Feng L., & Yang M. (2020). Multi-modal interactive fusion method for detecting teenagers’ psychological stress.J Biomed Inform, 106, 103427. doi: 10.1016/j.jbi.2020.103427
[49] Zuccarella-Hackl C., Jimenez-Gonzalo L., von Känel R., Princip M., Jellestad L., Langraf-Meister R. E., .. Ledermann K. (2023). Positive psychosocial factors and the development of symptoms of depression and posttraumatic stress symptoms following acute myocardial infarction.Front Psychol, 14, 1302699. doi: 10.3389/fpsyg.2023.1302699