Review of Depression Detection Using Social Media Text Data

doi:10.3778/j.issn.1002-8331.2209-0042

Abstract

Abstract: Machine learning has been gradually applied to depression detection using social media text data, and has prominently shown important application value in recent years. Firstly, this paper organizes and classifies social media text datasets, data preprocessing and machine learning methods used for depression detection. In addition, in terms of data feature representation, the basic feature representation, static and contextual word embedding are compared and analyzed. Secondly, this paper analyzes comprehensively the performance and characteristics of traditional machine learning with different basic features and different algorithm types as well as deep learning for depression detection. Finally, this paper summarizes and suggests further explorations in Chinese dataset creation, model interpretability, metaphor-based detection and lightweight pre-training model.

Key words: social media, text data, depression, machine learning

摘要： 近年来，机器学习被逐渐运用到基于社交媒体文本数据的抑郁症检测中并凸显重要应用价值。为梳理其应用现状和发展方向，对用于抑郁症检测的社交媒体文本数据集、数据预处理和机器学习方法进行整理分类。在数据特征表示方面，对比分析了基础特征表示、静态词嵌入和语境词嵌入。全面分析了利用不同基础特征和不同算法类型的传统机器学习以及深度学习进行抑郁症检测的性能和特点。总结并建议未来在中文数据集的创建、模型的可解释性、基于隐喻的检测和轻量级预训练模型方面做进一步的探索。

关键词: 社交媒体, 文本数据, 抑郁症, 机器学习

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.

References

[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.