Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (4): 54-63.DOI: 10.3778/j.issn.1002-8331.2209-0042
• Research Hotspots and Reviews • Previous Articles Next Articles
XU Dongdong, CAI Xiaohong, LIU Jing, CAO Hui
Online:
2023-02-15
Published:
2023-02-15
徐东东,蔡肖红,刘静,曹慧
XU Dongdong, CAI Xiaohong, LIU Jing, CAO Hui. Review of Depression Detection Using Social Media Text Data[J]. Computer Engineering and Applications, 2023, 59(4): 54-63.
徐东东, 蔡肖红, 刘静, 曹慧. 社交媒体文本数据的抑郁症检测研究综述[J]. 计算机工程与应用, 2023, 59(4): 54-63.
Add to citation manager EndNote|Ris|BibTeX
URL: http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2209-0042
[1] SHEN G,JIA J,NIE L,et al.Depression detection via harvesting social media:a multimodal dictionary learning solution[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence,2017:3838-3844. [2] FRIEDRICH M J.Depression is the leading cause of disability around the world[J].JAMA,2017,317(15):1517. [3] 区健新,吴寅,刘金婷,等.计算精神病学:抑郁症研究和临床应用的新视角[J].心理科学进展,2020,28(1):111-127. OU J X,WU Y,LIU J T,et al.Computational psychiatry:a new perspective on research and clinical applications in depression[J].Advances in Psychological Science,2020,28(1):111-127. [4] PARK M,CHA C,CHA M.Depressive moods of users portrayed in Twitter[C]//Proceedings of the 18th ACM International Conference on Knowledge Discovery and Data Mining,2012:1-8. [5] DE CHOUDHURY M,GAMON M,COUNTS S,et al.Predicting depression via social media[C]//Proceedings of the 7th International AAAI Conference on Weblogs and Social Media,2013. [6] SAXENA S,FUNK M,CHISHOLM D.World health assembly adopts comprehensive mental health action plan 2013—2020[J].The Lancet,2013,381(9882):1970-1971. [7] ZIEMER K S,KORKMAZ G.Using text to predict psychological and physical health:a comparison of human raters and computerized text analysis[J].Computers in Human Behavior,2017,76:122-127. [8] WONGKOLAP A,VADILLO M A,CURCIN V.Researching mental health disorders in the era of social media:systematic review[J].Journal of Medical Internet Research,2017,19(6). [9] ISLAM M,KABIR M A,AHMED A,et al.Depression detection from social network data using machine learning techniques[J].Health Information Science and Systems,2018,6. [10] CACHEDA F,FERNANDEZ D,NOVOA F J,et al.Early detection of depression:social network analysis and random forest techniques[J].Journal of Medical Internet Research,2019,21(6):e12554. [11] DE SOUZA FILHO E M,REY H C V,FRAJTAG R M,et al.Can machine learning be useful as a screening tool for depression in primary care?[J].Journal of Psychiatric Research,2021,132:1-6. [12] DOS SANTOS W R,FUNABASHI A M M,PARABONI I.Searching Brazilian Twitter for signs of mental health issues[C]//Proceedings of the 12th International Conference on Language Resources and Evaluation,2020:6111-6117. [13] WU M Y,SHEN C Y,WANG E T,et al.A deep architecture for depression detection using posting,behavior,and living environment data[J].Journal of Intelligent Information Systems,2020,54(2):225-244. [14] 董健宇,韦文棋,吴珂,等.机器学习在抑郁症领域的应用[J].心理科学进展,2020,28(2):266-274. DONG J Y,WEI W Q,WU K,et al.The application of machine learning in depression[J].Advances in Psychological Science,2020,28(2):266-274. [15] LOSADA D E,CRESTANI F.A test collection for research on depression and language use[C]//International Conference of the Cross-Language Evaluation Forum for European Languages.Cham:Springer,2016:28-39. [16] YATES A,COHAN A,GOHARIAN N.Depression and self-harm risk assessment in online forums[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing,2017:2968-2978. [17] LOSADA D E,CRESTANI F,PARAPAR J.eRISK 2017:CLEF lab on early risk prediction on the Internet:experimental foundations[C]//International Conference of the Cross-Language Evaluation Forum for European Languages.Cham:Springer,2017:346-360. [18] LOSADA D E,CRESTANI F,PARAPAR J.Overview of eRisk:early risk prediction on the Internet[C]//Proceedings of the 9th International Conference of the Cross-Language Evaluation Forum for European Languages,2018:343-361. [19] COPPERSMITH G,DREDZE M,HARMAN C,et al.CLPsych 2015 shared task:depression and PTSD on Twitter[C]//Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology,2015:31-39. [20] NGUYEN T,VENKATESH S,PHUNG D.Textual cues for online depression in community and personal settings[C]//Proceedings of the 12th International Conference on Advanced Data Mining and Applications,2016:19-34. [21] FATIMA I,MUKHTAR H,AHMAD H F,et al.Analysis of user-generated content from online social communities to characterise and predict depression degree[J].Journal of Information Science,2018,44(5):683-695. [22] HAN S,HUANG H,TANG Y.Knowledge of words:an interpretable approach for personality recognition from social media[J].Knowledge-Based Systems,2020,194:105550. [23] PRIETO V M,MATOS S,ALVAREZ M,et al.Twitter:a good place to detect health conditions[J].PLoS One,2014,9(1):e86191. [24] NGUYEN T,O’DEA B,LARSEN M,et al.Using linguistic and topic analysis to classify sub-groups of online depression communities[J].Multimedia Tools and Applications,2017,76(8):10653-10676. [25] CHEN X,SYKORA M D,JACKSON T W,et al.Tweeting your mental health:exploration of different classifiers and features with emotional signals in identifying mental health conditions[C]//Proceedings of the 51st Annual Hawaii International Conference on System Sciences,2018:3320-3328. [26] LEIVA V,FREIRE A.Towards suicide prevention:early detection of depression on social media[C]//Proceedings of the 4th International Conference on Internet Science,2017:428-436. [27] HU Q,LI A,HENG F,et al.Predicting depression of social media user on different observation windows[C]//Proceedings of the 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology,2015:361-364. [28] PENG Z,HU Q,DANG J.Multi-kernel SVM based depression recognition using social media data[J].International Journal of Machine Learning and Cybernetics,2019,10(1):43-57. [29] LIU J,SHI M.A hybrid feature selection and ensemble approach to identify depressed users in online social media[J].Frontiers in Psychology,2021,12:802821. [30] TARIQ S,AKHTAR N,AFZAL H,et al.A novel co-training-based approach for the classification of mental illnesses using social media posts[J].IEEE Access,2019,7:166165-166172. [31] BURDISSO S G,ERRECALDE M,MONTES-Y-GOMEZ M.A text classification framework for simple and effective early depression detection over social media streams[J].Expert Systems with Applications,2019,133:182-197. [32] BRIAND A,ALMEIDA H,MEURS M J.Analysis of social media posts for early detection of mental health conditions[C]//Proceedings of the 31st Canadian Conference on Artificial Intelligence,2018:133-143. [33] TROTZEK M,KOITKA S,FRIEDRICH C M.Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences[J].IEEE Transactions on Knowledge and Data Engineering,2018,32(3):588-601. [34] KIM J,LEE J,PARK E,et al.A deep learning model for detecting mental illness from user content on social media[J].Scientific Reports,2020,10(1):1-6. [35] RAO G,ZHANG Y,ZHANG L,et al.MGL-CNN:a hierarchical posts representations model for identifying depressed individuals in online forums[J].IEEE Access,2020,8:32395-32403. [36] AMANAT A,RIZWAN M,JAVED A R,et al.Deep learning for depression detection from textual data[J].Electronics,2022,11(5):676. [37] AHMAD H,ASGHAR M Z,ALOTAIBI F M,et al.Applying deep learning technique for depression classification in social media text[J].Journal of Medical Imaging and Health Informatics,2020,10(10):2446-2451. [38] CONG Q,FENG Z,LI F,et al.X-A-BiLSTM:a deep learning approach for depression detection in imbalanced data[C]//Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine-Human Genomics,2018:1624-1627. [39] ARAGóN M E,LóPEZ-MONROY A P,GONZáLEZ L C,et al.Attention to emotions:detecting mental disorders in social media[C]//Proceedings the 23rd of International Conference on Text,Speech,and Dialogue,2020:231-239. [40] ZOGAN H,RAZZAK I,JAMEEL S,et al.DepressionNet:a novel summarization boosted deep framework for depression detection on social media[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval,2021:133-142. [41] MAGA?A D.Cultural competence and metaphor in mental healthcare interactions:a linguistic perspective[J].Patient Education and Counseling,2019,102(12):2192-2198. [42] LLEWELLYN-BEARDSLEY J,RENNICK-EGGLESTONE S,CALLARD F,et al.Characteristics of mental health recovery narratives:systematic review and narrative synthesis[J].PLoS one,2019,14(3):e0214678. [43] ZHANG D,SHI N,PENG C,et al.MAM:a metaphor-based approach for mental illness detection[C]//Proceedings of the 21st International Conference on Computational Science,2021:570-583. [44] GONG H,GUPTA K,JAIN A,et al.IlliniMet:illinois system for metaphor detection with contextual and linguistic information[C]//Proceedings of the 2nd Workshop on Figurative Language Processing,2020:146-153. [45] MAO R,LIN C,GUERIN F.End-to-end sequential metaphor identification inspired by linguistic theories[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics,2019:3888-3898. [46] ALMARS A M.Attention-based Bi-LSTM model for Arabic depression classification[J].Computers Materials & Continua,2022,71(2):3091-3106. [47] REN L,LIN H,XU B,et al.Depression detection on reddit with an emotion-based attention network:algorithm development and validation[J].JMIR Medical Informatics,2021,9(7):e28754. [48] SONG H,YOU J,CHUNG J W,et al.Feature attention network:interpretable depression detection from social media[C]//Proceedings of the 32nd Pacific Asia Conference on Language,Information and Computation,2018. [49] UBAN A S,CHULVI B,ROSSO P.On the explainability of automatic predictions of mental disorders from social media data[C]//Proceedings of the 26th International Conference on Applications of Natural Language to Information Systems,2021:301-314. [50] ZOGAN H,RAZZAK I,WANG X,et al.Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media[J].World Wide Web,2022,25(1):281-304. [51] YADAV S,CHAUHAN J,SAIN J P,et al.Identifying depressive symptoms from tweets:figurative language enabled multitask learning framework[C]//Proceedings of the 28th International Committee on Computational Linguistics,2020:696-709. [52] WANG X,CHEN S,LI T,et al.Depression risk prediction for Chinese microblogs via deep-learning methods:content analysis[J].JMIR Medical Informatics,2020,8(7):e17958. [53] ZEBERGA K,ATTIQUE M,SHAH B,et al.A novel text mining approach for mental health prediction using Bi-LSTM and BERT model[J].Computational Intelligence and Neuroscience,2022:7893775. [54] KHAN P I,RAZZAK I,DENGEL A,et al.Performance comparison of transformer-based models on twitter health mention classification[J].IEEE Transactions on Computational Social Systems,2022.DOI:10.1109/TCSS.2022. 3143768. [55] ORABI A H,BUDDHITHA P,ORABI M H,et al.Deep learning for depression detection of Twitter users[C]//Proceedings of the 5th Workshop on Computational Linguistics and Clinical Psychology:From Keyboard to Clinic,2018:88-97. |
[1] | PEI Wenbin, WANG Hailong, LIU Lin, PEI Dongmei. Review of Musical Instrument Recognition in Music Information Retrieval [J]. Computer Engineering and Applications, 2023, 59(2): 34-47. |
[2] | WANG Yu, WANG Xin, ZHANG Shujuan, ZHENG Guoqiang, ZHAO Long, ZHENG Gaofeng. Research on Efficient Knowledge Fusion Method for Heterogeneous Big Data Environments [J]. Computer Engineering and Applications, 2022, 58(6): 142-148. |
[3] | LU Bingjie, LI Weizhuo, NA Chongning, NIU Zuoyao, CHEN Kui. Survey of Auto Insurance Fraud Detection with Machine Learning Models [J]. Computer Engineering and Applications, 2022, 58(5): 34-49. |
[4] | ZHAO Zhenzhen, DONG Yanru, CAO Hui, CAO Bin. Research Status of Elderly Fall Detection Algorithms [J]. Computer Engineering and Applications, 2022, 58(5): 50-65. |
[5] | HUANG Yanqian, CHI Dongxiang, XU Lingling. Research on Few-Shot Learning Based on Embedding Learning [J]. Computer Engineering and Applications, 2022, 58(3): 34-49. |
[6] | HU Zhenwei, WANG Tinghua, ZHOU Huiying. Review of Feature Selection Methods Based on Kernel Statistical Independence Criteria [J]. Computer Engineering and Applications, 2022, 58(22): 54-64. |
[7] | WANG Yixin, ZHU Xiangru, YANG Lijun. EEG Depression Recognition Based on Feature Fusion of Common Spatial Pattern and Brain Connectivity [J]. Computer Engineering and Applications, 2022, 58(22): 150-158. |
[8] | LI Yunlong, QING Linbo, HAN Longmei, WANG Yuchen. Survey on Visual Affordance Research [J]. Computer Engineering and Applications, 2022, 58(18): 1-15. |
[9] | SUN Shukui, FAN Jing, QU Jinshuai, LU Peidong. Survey of Generative Adversarial Networks [J]. Computer Engineering and Applications, 2022, 58(18): 90-103. |
[10] | FENG Jun, LI Yan, HANG Tingting. Survey on Question Decomposition Method in Question Answering System [J]. Computer Engineering and Applications, 2022, 58(17): 23-33. |
[11] | ZHOU Huiying, WANG Tinghua, ZHANG Daili. Research Progress of Multi-Label Feature Selection [J]. Computer Engineering and Applications, 2022, 58(15): 52-67. |
[12] | SUN Chao, WEN Min, LI Pengzu, LI Yao, Ibegbu Nnamdi JULIAN, GUO Hao. Feature Extraction and Classification of Uncertain Brain Network Based on Relative Range [J]. Computer Engineering and Applications, 2022, 58(14): 126-133. |
[13] | NIU Hongli, ZHAO Yazhi. Predicting Stock Price Index Using Bagging Algorithm and GRU Model [J]. Computer Engineering and Applications, 2022, 58(12): 132-138. |
[14] | XIE Xin, ZHANG Xianyong, YANG Jilin. Decision Tree Algorithm Fusing Information Gain and Gini Index [J]. Computer Engineering and Applications, 2022, 58(10): 139-144. |
[15] | ZHA Guoqing, HU Chaoran, SUN Mingtao, WANG Deqing. Depression Group’s Internet Social Interactionand Preliminary Screening Algorithm for Weibo with Suspected Depression [J]. Computer Engineering and Applications, 2022, 58(1): 158-164. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||