research-article

Towards Mental Health Analysis in Social Media for Low-resourced Languages

Author:

Muskan GargAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 23, Issue 3

Article No.: 47, Pages 1 - 22

https://doi.org/10.1145/3638761

Published: 09 March 2024 Publication History

Abstract

The surge in internet use for expression of personal thoughts and beliefs has made it increasingly feasible for the social Natural Language Processing (NLP) research community to find and validate associations between social media posts and mental health status. Cross-sectional and longitudinal studies of low-resourced social media data bring to fore the importance of real-time responsible Artificial Intelligence (AI) models for mental health analysis in native languages. Aiming at classifying research for social computing and tracking advances in the development of learning-based models, we propose a comprehensive survey on mental health analysis for social media and posit the need of analyzing low-resourced social media data for mental health. We first classify three components for computing on social media as: SM- data mining/natural language processing on social media, IA- integrated applications with social media data and user-network modeling, and NM- user and network modeling on social networks. To this end, we posit the need of mental health analysis in different languages of East Asia (e.g., Chinese, Japanese, Korean), South Asia (Hindi, Bengali, Tamil), Southeast Asia (Malay, Thai, Vietnamese), European languages (Spanish, French) and the Middle East (Arabic). Our comprehensive study examines available resources and recent advances in low-resourced languages for different aspects of SM, IA, and NM to discover new frontiers as potential field of research.

References

[1]

Muhammad Abdul-Mageed and Lyle Ungar. 2017. Emonet: Fine-grained emotion detection with gated recurrent neural networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 718–728.

[2]

Carlos Aguirre, Keith Harrigian, and Mark Dredze. 2021. Gender and racial fairness in depression research using social media. In 16th Conference of the European Chapter of the Associationfor Computational Linguistics (EACL’21). Association for Computational Linguistics (ACL), 2932–2949.

[3]

Nadeem Akhtar and Mohd Vasim Ahamad. 2021. Graph tools for social network analysis. In Proceedings of the Research Anthology on Digital Transformation, Organizational Change, and the Impact of Remote Work. IGI Global, 485–500.

[4]

Hayda Almeida, Antoine Briand, and Marie-Jean Meurs. 2017. Detecting early risk of depression from social media user-generated content. In Proceedings of the CLEF (Working Notes).

[5]

Amna Amanat, Muhammad Rizwan, Abdul Rehman Javed, Maha Abdelhaq, Raed Alsaqour, Sharnil Pandya, and Mueen Uddin. 2022. Deep learning for depression detection from textual data. Electronics 11, 5 (2022), 676.

[6]

Gunjan Ansari, Muskan Garg, and Chandni Saxena. 2021. Data augmentation for mental health classification on social media. In Proceedings of the 18th International Conference on Natural Language Processing (ICON). 152–161.

[7]

Emmi Bevensee, Maxwell Aliapoulios, Quinn Dougherty, Jason Baumgartner, Damon Mccoy, and Jeremy Blackburn. 2020. SMAT: The social media analysis toolkit. In Proceedings of the 14th International AAAI Conference on Web and Social Media.

[8]

Lei Cao, Huijun Zhang, and Ling Feng. 2020. Building and using personal knowledge graph to improve suicidal ideation detection on social media. IEEE Transactions on Multimedia (2020).

[9]

Lei Cao, Huijun Zhang, Ling Feng, Zihan Wei, Xin Wang, Ningyun Li, and Xiaohao He. 2019. Latent suicide risk detection on microblog via suicide-oriented word embeddings and layered attention. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)1718–1728.

[10]

Junyeop Cha, Seoyun Kim, and Eunil Park. 2022. A lexicon-based approach to examine depression detection in social media: The case of Twitter and university community. Humanities and Social Sciences Communications 9, 1 (2022), 1–10.

[11]

Arman Cohan, Bart Desmet, Andrew Yates, Luca Soldaini, Sean MacAvaney, and Nazli Goharian. 2018. SMHD: A large-scale resource for exploring online language usage for multiple mental health conditions. In 27th International Conference on Computational Linguistics. ACL, 1485–1497.

[12]

David Combe, Christine Largeron, Elod Egyed-Zsigmond, and Mathias Géry. 2010. A comparative study of social network analysis tools. In Web Intelligence and Virtual Enterprises.

[13]

Qing Cong, Zhiyong Feng, Fang Li, Yang Xiang, Guozheng Rao, and Cui Tao. 2018. XA-BiLSTM: A deep learning approach for depression detection in imbalanced data. In IEEE International Conference on Bioinformatics and Biomedicine. IEEE, 1624–1627.

[14]

Glen Coppersmith, Mark Dredze, and Craig Harman. 2014. Quantifying mental health signals in Twitter. In Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 51–60.

[15]

Glen Coppersmith, Mark Dredze, Craig Harman, Kristy Hollingshead, and Margaret Mitchell. 2015. CLPsych 2015 shared task: Depression and PTSD on Twitter. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 31–39.

[16]

Fabio Crestani. 2021. Overview of eRisk 2021: Early risk prediction on the internet. In Experimental IR Meets Multilinguality, Multimodality, and Interaction: 12th International Conference of the CLEF Association, CLEF 2021, Virtual Event, September 21–24, 2021, Proceedings, Vol. 12880. Springer Nature, 324.

[17]

Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. 2013. Predicting depression via social media. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 7.

[18]

Sandro Galea, Raina M Merchant, and Nicole Lurie. 2020. The mental health consequences of COVID-19 and physical distancing: The need for prevention and early intervention. JAMA internal medicine 180, 6 (2020), 817–818.

[19]

Muskan Garg. 2021. A survey on different dimensions for graphical keyword extraction techniques. Artificial Intelligence Review (2021), 1–40.

[20]

Muskan Garg. 2023. Mental disturbance impacting wellness dimensions: Resources and open research directions. Asian Journal of Psychiatry (2023), 103876. DOI:

[21]

Muskan Garg. 2023. Mental health analysis in social media posts: A survey. Archives of Computational Methods in Engineering 30, 3 (2023), 1819–1842.

[22]

Muskan Garg. 2023. Multi-class categorization of reasons behind mental disturbance in long texts. Knowledge-Based Systems 276 (2023), 110742.

[23]

Muskan Garg. 2023. The social media psyche: Modeling mental disturbance in the digital age. In Software Principles of Design for Data Modeling. IGI Global, 1–13.

[24]

Muskan Garg. 2023. WellXplain: Wellness concept extraction and classification in reddit posts for mental health analysis. Knowledge-Based Systems (2023), 111228.

[25]

Muskan Garg, Manas Gaur, Raxit Goswami, and Sunghwan Sohn. 2023. LOST: A mental health dataset of low self-esteem in reddit posts. Proceedings of IEEE Systems, Man, and Cybenetics (2023).

[26]

Muskan Garg, Chandni Saxena, Sriparna Saha, Veena Krishnan, Ruchi Joshi, and Vijay Mago. 2022. CAMS: An annotated corpus for causal analysis of mental health issues in social media posts. In Proceedings of the Thirteenth Language Resources and Evaluation Conference. 6387–6396.

[27]

Muskan Garg, Chandni Saxena, Veena Krishnan, Ruchi Joshi, Sriparna Saha, Vijay Mago, and Bonnie J Dorr. 2022. CAMS: An annotated corpus for causal analysis of mental health issues in social media posts. arXiv:2207.04674. Retrieved from https://arxiv.org/abs/2207.04674

[28]

Muskan Garg, Chandni Saxena, Debabrata Samanta, and Bonnie J Dorr. 2023. LonXplain: Lonesomeness as a Consequence of Mental Disturbance in Reddit Posts. In International Conference on Applications of Natural Language to Information Systems. Springer, 379–390.

Digital Library

[29]

Muskan Garg, Amirmohammad Shahbandegan, Amrit Chadha, and Vijay Mago. 2023. An annotated dataset for explainable interpersonal risk factors of mental disturbance in social media posts. In Findings of Association for Computational Linguistics (ACL).

[30]

Muskan Garg and Sunghwan Sohn. 2023. CareD: Caregiver’s experience with cognitive decline in reddit posts. In 2023 IEEE 11th International Conference on Healthcare Informatics (ICHI). IEEE, 581–587.

[31]

Tapotosh Ghosh, Md Hasan Al Banna, Md Jaber Al Nahian, Mohammed Nasir Uddin, M Shamim Kaiser, and Mufti Mahmud. 2023. An attention-based hybrid architecture with explainability for depressive social media text detection in Bangla. Expert Systems with Applications 213 (2023), 119007.

Digital Library

[32]

Deanna Arissa Mohd Osman Giani and NurFarhana Ardillah Aftar. 2022. Psychometric properties of the self-consciousness scale-revised: A malay translation. JURNAL PSIKOLOGI MALAYSIA 36, 1 (2022).

[33]

George Gkotsis, Anika Oellrich, Tim Hubbard, Richard Dobson, Maria Liakata, Sumithra Velupillai, and Rina Dutta. 2016. The language of mental health problems in social media. In Proceedings of the 3rd Workshop on Computational Linguistics and Clinical Psychology. 63–73.

[34]

Kristina Gligorić, Ashton Anderson, and Robert West. 2018. How constraints affect content: The case of Twitter’s switch from 140 to 280 characters. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 12.

[35]

Tao Gui, Liang Zhu, Qi Zhang, Minlong Peng, Xu Zhou, Keyu Ding, and Zhigang Chen. 2019. Cooperative multimodal approach to depression detection in Twitter. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 110–117.

Digital Library

[36]

Sharath Chandra Guntuku, Anneke Buffone, Kokil Jaidka, Johannes C. Eichstaedt, and Lyle H. Ungar. 2019. Understanding and measuring psychological stress using social media. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 13, 214–225.

[37]

Sharath Chandra Guntuku, David B. Yaden, Margaret L. Kern, Lyle H. Ungar, and Johannes C. Eichstaedt. 2017. Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences 18 (2017), 43–49.

[38]

Divya Gupta, MPS Bhatia, and Akshi Kumar. 2021. Real-time mental health analytics using IoMT and social media datasets: Research and challenges. Available at SSRN 3842818 (2021).

[39]

Rabia Habiba, Dr Muhammad Awais, and Dr Muhammad Shoaib. 2020. A technique to calculate national happiness index by analyzing roman urdu messages posted on social media. ACM Transactions on Asian and Low-Resource Language Information Processing 19, 6 (2020), 1–16.

Digital Library

[40]

Mika Hämäläinen, Pattama Patpong, Khalid Alnajjar, Niko Partanen, and Jack Rueter. 2021. Detecting depression in thai blog posts: A dataset and a baseline. In Proceedings of the 7th Workshop on Noisy User-generated Text. 20–25.

[41]

Ayaan Haque, Viraaj Reddi, and Tyler Giallanza. 2021. Deep learning for suicide and depression identification with unsupervised label correction. In Proceedings of the International Conference on Artificial Neural Networks. Springer, 436–447.

Digital Library

[42]

Keith Harrigian, Carlos Aguirre, and Mark Dredze. 2021. On the state of social media data for mental health research. NAACL HLT 2021 (2021), 15.

[43]

Misato Hiraga. 2017. Predicting depression for japanese blog text. In Proceedings of the ACL 2017, Student Research Workshop. 107–113.

[44]

Dirk Hovy and Shannon L. Spruit. 2016. The social impact of natural language processing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 591–598.

[45]

Jamil Hussain, Fahad Ahmed Satti, Muhammad Afzal, Wajahat Ali Khan, Hafiz Syed Muhammad Bilal, Muhammad Zaki Ansaar, Hafiz Farooq Ahmad, Taeho Hur, Jaehun Bang, and Jee-In Kim. 2020. Exploring the dominant features of social media for depression detection. Journal of Information Science 46, 6 (2020), 739–759.

Digital Library

[46]

Julia Ive, George Gkotsis, Rina Dutta, Robert Stewart, and Sumithra Velupillai. 2018. Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health. In Proceedings of the 5th Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic. 69–77.

[47]

Ayoub Jannani, Nawal Sael, and Faouzia Benabbou. 2021. Predicting quality of life using machine learning: Case of world happiness index. In Proceedings of the 2021 4th International Symposium on Advanced Electrical and Communication Technologies. IEEE, 1–6.

[48]

Shaoxiong Ji, Celina Ping Yu, Sai-fu Fung, Shirui Pan, and Guodong Long. 2018. Supervised learning for suicidal ideation detection in online user content. Complexity 2018 (2018).

Digital Library

[49]

Shaoxiong Ji, Tianlin Zhang, Luna Ansari, Jie Fu, Prayag Tiwari, and Erik Cambria. 2021. MentalBERT: Publicly available pretrained language models for mental healthcare. arXiv:2110.15621. Retrieved from https://arxiv.org/abs/2110.15621

[50]

Shaoxiong Ji, Tianlin Zhang, Luna Ansari, Jie Fu, Prayag Tiwari, and Erik Cambria. 2022. MentalBERT: Publicly available pretrained language models for mental healthcare. In Proceedings of the 13th Language Resources and Evaluation Conference. 7184–7190.

[51]

Muhammad Khubayeeb Kabir, Maisha Islam, Anika Nahian Binte Kabir, Adiba Haque, and Md. Khalilur Rhaman. 2022. Detection of depression severity using bengali social media posts on mental health: Study using natural language processing techniques. JMIR Formative Research 6, 9 (2022), e36118.

[52]

Kantinee Katchapakirin, Konlakorn Wongpatikaseree, Panida Yomaboot, and Yongyos Kaewpitakkun. 2018. Facebook social media for depression detection in the Thai community. In Proceedings of the 2018 15th International Joint Conference on Computer Science and Software Engineering. IEEE, 1–6.

[53]

Tugberk Kaya. 2020. The changes in the effects of social media use of Cypriots due to COVID-19 pandemic. Technology in Society 63 (2020), 101380.

[54]

Shahid Nawaz Khan, Maitree Leekha, Jainendra Shukla, and Rajiv Ratn Shah. 2020. Vyaktitv: A multimodal peer-to-peer hindi conversations based dataset for personality assessment. In Proceedings of the 2020 IEEE 6th International Conference on Multimedia Big Data. IEEE, 103–111.

[55]

Kazi Zainab Khanam, Gautam Srivastava, and Vijay Mago. 2022. The homophily principle in social network analysis: A survey. Multimedia Tools and Applications (2022), 1–44.

[56]

E. David Klonsky and Titania Dixon-Luinenburg. 2022. Mental disorders and beyond in the quest to understand suicide: A reply to Joiner and Robison. Archives of Suicide Research (2022), 1–4.

[57]

E. David Klonsky and Alexis M. May. 2015. The three-step theory (3ST): A new theory of suicide rooted in the “ideation-to-action” framework. International Journal of Cognitive Therapy 8, 2 (2015), 114–129.

[58]

Daeun Lee, Migyeong Kang, Minji Kim, and Jinyoung Han. 2022. Detecting suicidality with a contextual graph neural network. In Proceedings of the 8th Workshop on Computational Linguistics and Clinical Psychology. 116–125.

[59]

Keren Lehavot, Dror Ben-Zeev, and Robin E. Neville. 2012. Ethical considerations and social media: A case of suicidal postings on Facebook. Journal of Dual Diagnosis 8, 4 (2012), 341–346.

[60]

Victor Leiva and Ana Freire. 2017. Towards suicide prevention: Early detection of depression on social media. In Proceedings of the International Conference on Internet Science. Springer, 428–436.

Digital Library

[61]

Chenhao Lin, Pengwei Hu, Hui Su, Shaochun Li, Jing Mei, Jie Zhou, and Henry Leung. 2020. Sensemood: Depression detection on social media. In Proceedings of the 2020 International Conference on Multimedia Retrieval. 407–411.

Digital Library

[62]

Huijie Lin, Jia Jia, Quan Guo, Yuanyuan Xue, Jie Huang, Lianhong Cai, and Ling Feng. 2014. Psychological stress detection from cross-media microblog data using deep sparse neural network. In Proceedings of the 2014 IEEE International Conference on Multimedia and Expo. IEEE, 1–6.

[63]

Huijie Lin, Jia Jia, Quan Guo, Yuanyuan Xue, Qi Li, Jie Huang, Lianhong Cai, and Ling Feng. 2014. User-level psychological stress detection from social media using deep neural network. In Proceedings of the 22nd ACM International Conference on Multimedia. 507–516.

Digital Library

[64]

Huijie Lin, Jia Jia, Jiezhong Qiu, Yongfeng Zhang, Guangyao Shen, Lexing Xie, Jie Tang, Ling Feng, and Tat-Seng Chua. 2017. Detecting stress based on social interactions in social networks. IEEE Transactions on Knowledge and Data Engineering 29, 9 (2017), 1820–1833.

Digital Library

[65]

David E. Losada, Fabio Crestani, and Javier Parapar. 2018. Overview of eRisk: Early risk prediction on the internet. In Proceedings of the International Conference of the Cross-language Evaluation Forum for European Languages. Springer, 343–361.

[66]

Yihua Ma and Youliang Cao. 2020. Dual attention based suicide risk detection on social media. In Proceedings of the 2020 IEEE International Conference on Artificial Intelligence and Computer Applications. IEEE, 637–640.

[67]

Naoki Masuda, Issei Kurahashi, and Hiroko Onari. 2013. Suicide ideation of individuals in online social networks. PloS One 8, 4 (2013), e62262.

[68]

Matthew Matero, Akash Idnani, Youngseo Son, Salvatore Giorgi, Huy Vu, Mohammad Zamani, Parth Limbachiya, Sharath Chandra Guntuku, and H. Andrew Schwartz. 2019. Suicide risk assessment with multi-level dual-context language and BERT. In Proceedings of the 6th Workshop on Computational Linguistics and Clinical Psychology. 39–44.

[69]

Puneet Mathur, Ramit Sawhney, Shivang Chopra, Maitree Leekha, and Rajiv Ratn Shah. 2020. Utilizing temporal psycholinguistic cues for suicidal intent estimation. In Proceedings of the European Conference on Information Retrieval. Springer, 265–271.

Digital Library

[70]

Catherine M. McHugh, Amy Corderoy, Christopher James Ryan, Ian B. Hickie, and Matthew Michael Large. 2019. Association between suicidal ideation and suicide: Meta-analyses of odds ratios, sensitivity, specificity and positive predictive value. BJPsych open 5, 2 (2019).

[71]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv:1301.3781. Retrieved from https://arxiv.org/abs/1301.3781

[72]

Ritwik Mishra, Simranjeet Singh, Jasmeet Kaur, Pushpendra Singh, and Rajiv Shah. 2023. Hindi chatbot for supporting maternal and child health related queries in rural india. In Proceedings of the 5th Clinical Natural Language Processing Workshop. 69–77.

[73]

Rohan Mishra, Pradyumn Prakhar Sinha, Ramit Sawhney, Debanjan Mahata, Puneet Mathur, and Rajiv Ratn Shah. 2019. SNAP-BATNET: Cascading author profiling and social network graphs for suicide ideation detection on social media. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop. 147–156.

[74]

Margaret Mitchell, Kristy Hollingshead, and Glen Coppersmith. 2015. Quantifying the language of schizophrenia in social media. In Proceedings of the 2nd workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 11–20.

[75]

Carmel Mary Belinda MJ, S. Ravikumar, Muhammad Arif, V. Dhilip Kumar, Antony Kumar, and G. Arulkumaran. 2022. Linguistic analysis of hindi-english mixed tweets for depression detection. Journal of Mathematics 2022 (2022), 1–7.

[76]

Seungwhan Moon, Leonardo Neves, and Vitor Carvalho. 2018. Multimodal named entity disambiguation for noisy social media posts. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2000–2008.

[77]

Yutaka Miyaji Yuka Niimi. 2021. Machine learning approach for depression detection in japanese. In Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation. 346–353.

[78]

Susan Nolen-Hoeksema. 1991. Responses to depression and their effects on the duration of depressive episodes. Journal of Abnormal Psychology 100, 4 (1991), 569.

[79]

Jaime Noriega and Edward Blair. 2008. Advertising to bilinguals: Does the language of advertising influence the nature of thoughts? Journal of Marketing 72, 5 (2008), 69–83.

[80]

Nursalam Nursalam, Martha Octavia, Rr Dian Tristiana, and Ferry Efendi. 2019. Association between insomnia and social network site use in Indonesian adolescents. In Proceedings of the Nursing forum. Wiley Online Library, 149–156.

[81]

Bridianne O’dea, Mark E. Larsen, Philip J. Batterham, Alison L. Calear, and Helen Christensen. 2017. A linguistic analysis of suicide-related Twitter posts. Crisis: The Journal of Crisis Intervention and Suicide Prevention 38, 5 (2017), 319.

[82]

Kelechi Ogueji, Yuxin Zhu, and Jimmy Lin. 2021. Small data? no problem! exploring the viability of pretrained multilingual language models for low-resourced languages. In Proceedings of the 1st Workshop on Multilingual Representation Learning. 116–126.

[83]

Ahmed Husseini Orabi, Prasadith Buddhitha, Mahmoud Husseini Orabi, and Diana Inkpen. 2018. Deep learning for depression detection of twitter users. In Proceedings of the 5th Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic. 88–97.

[84]

Victoria Pachón, Jacinto Mata Vázquez, and Juan Luís Domínguez Olmedo. 2021. Identification of profession and occupation in health-related social media using tweets in spanish. In Proceedings of the 6th Social Media Mining for Health (# SMM4H) Workshop and Shared Task. 105–107.

[85]

Minsu Park, Chiyoung Cha, and Meeyoung Cha. 2012. Depressive moods of users portrayed in Twitter. (2012).

[86]

Minsu Park, David McDonald, and Meeyoung Cha. 2013. Perception differences between the depressed and non-depressed users in twitter. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 7.

[87]

Seojeong Park, Soobin Lee, Woo Jung Kim, and Min Song. 2022. A deep learning-based depression trend analysis of korean on social media. Journal of the Korean Society for information Management 39, 1 (2022), 91–117.

[88]

Inna Pirina and Çağrı Çöltekin. 2018. Identifying depression on reddit: The effect of training data. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop and Shared Task. 9–12.

[89]

Robert Plutchik. 1980. A general psychoevolutionary theory of emotion. In Proceedings of the Theories of Emotion. Elsevier, 3–33.

[90]

Daniel Preoţiuc-Pietro, Johannes Eichstaedt, Gregory Park, Maarten Sap, Laura Smith, Victoria Tobolsky, H. Andrew Schwartz, and Lyle Ungar. 2015. The role of personality, age, and gender in tweeting about mental illness. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 21–30.

[91]

Erin Pullen, Emily A. Ekl, Elizabeth Feliz, Christopher Turner, Brea L. Perry, and Bernice A. Pescosolido. 2022. Labeling, causal attributions, and social network ties to people with mental illness. Social Science and Medicine 293 (2022), 114646.

[92]

Philip Resnik, William Armstrong, Leonardo Claudino, and Thang Nguyen. 2015. The university of maryland CLPsych 2015 shared task system. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 54–60.

[93]

Philip Resnik, William Armstrong, Leonardo Claudino, Thang Nguyen, Viet-An Nguyen, and Jordan Boyd-Graber. 2015. Beyond LDA: Exploring supervised topic modeling for depression-related language in Twitter. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 99–107.

[94]

Antoine Rolet, Marco Cuturi, and Gabriel Peyré. 2016. Fast dictionary learning with a smoothed Wasserstein loss. In Proceedings of the Artificial Intelligence and Statistics. PMLR, 630–638.

[95]

Ramadani Anwar Sabaruddin and Suhaila Saee. 2021. Malay Tweets: Discovering mental health situation during COVID-19 Pandemic in Malaysia. In Proceedings of the 2021 IEEE 19th Student Conference on Research and Development. IEEE, 58–63.

[96]

Ryota Sakurai, Yuta Nemoto, Hiroko Mastunaga, and Yoshinori Fujiwara. 2021. Who is mentally healthy? Mental health profiles of Japanese social networking service users with a focus on LINE, Facebook, Twitter, and Instagram. Plos one 16, 3 (2021), e0246090.

[97]

Ramit Sawhney, Harshit Joshi, Lucie Flek, and Rajiv Shah. 2021. PHASE: Learning emotional phase-aware representations for suicide ideation detection on social media. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2415–2428.

[98]

Ramit Sawhney, Harshit Joshi, Saumya Gandhi, and Rajiv Ratn Shah. 2021. Towards ordinal suicide ideation detection on social media. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 22–30.

Digital Library

[99]

Ramit Sawhney, Harshit Joshi, Rajiv Shah, and Lucie Flek. 2021. 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. 2176–2190.

[100]

Ramit Sawhney, Prachi Manchanda, Puneet Mathur, Rajiv Shah, and Raj Singh. 2018. 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. 167–175.

[101]

Harold Schlosberg. 1954. Three dimensions of emotion. Psychological Review 61, 2 (1954), 81.

[102]

H. Andrew Schwartz, Salvatore Giorgi, Maarten Sap, Patrick Crutchley, Lyle Ungar, and Johannes Eichstaedt. 2017. Dlatk: Differential language analysis toolkit. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 55–60.

[103]

Katrin Seeher, Rodrigo Cataldi, Tarun Dua, and Devora Kestel. 2023. Inequitable Access to Dementia Diagnosis and Care in Low-Resource Settings–A Global Perspective. 133–137.

[104]

Faisal Muhammad Shah, Farsheed Haque, Ragib Un Nur, Shaeekh Al Jahan, and Zarar Mamud. 2020. A hybridized feature extraction approach to suicidal ideation detection from social media post. In Proceedings of the 2020 IEEE Region 10 Symposium. IEEE, 985–988.

[105]

Vimal K. Sharma, Savita Jagawat, Aarti Midha, Anil Jain, Anil Tambi, Leena Kumari Mangwani, Bhawna Sharma, Parul Dubey, Vipin Satija, and John R. M. Copeland. 2010. The global mental health assessment tool-validation in hindi: A validity and feasibility study. Indian Journal of Psychiatry 52, 4 (2010), 316.

[106]

Guangyao Shen, Jia Jia, Liqiang Nie, Fuli Feng, Cunjun Zhang, Tianrui Hu, Tat-Seng Chua, and Wenwu Zhu. 2017. Depression detection via harvesting social media: A multimodal dictionary learning solution. In Proceedings of the IJCAI. 3838–3844.

[107]

Tiancheng Shen, Jia Jia, Guangyao Shen, Fuli Feng, Xiangnan He, Huanbo Luan, Jie Tang, Thanassis Tiropanis, Tat Seng Chua, and Wendy Hall. 2018. Cross-domain depression detection via harvesting social media. International joint conferences on artificial intelligence.

[108]

Eli Sherman, Keith Harrigian, Carlos Aguirre, and Mark Dredze. 2021. Towards understanding the role of gender in deploying social media-based mental health surveillance models. In Proceedings of the 7th Workshop on Computational Linguistics and Clinical Psychology: Improving Access. 217–223.

[109]

Grigori Sidorov, Sabino Miranda-Jiménez, Francisco Viveros-Jiménez, Alexander Gelbukh, Noé Castro-Sánchez, Francisco Velásquez, Ismael Díaz-Rangel, Sergio Suárez-Guerra, Alejandro Trevino, and Juan Gordon. 2012. Empirical study of machine learning based approach for opinion mining in tweets. In Proceedings of the Mexican International Conference on Artificial Intelligence. Springer, 1–14.

[110]

Vicki Lynn Simpson, Zachary Joseph Hass, Jitesh Panchal, and Bethany McGowan. 2022. Understanding the development, evaluation, and sustainability of community health networks using social network analysis: A scoping review. American Journal of Health Promotion 36, 2 (2022), 318–327.

[111]

Gopendra Vikram Singh, Priyanshu Priya, Mauajama Firdaus, Asif Ekbal, and Pushpak Bhattacharyya. 2022. EmoInHindi: A multi-label emotion and intensity annotated dataset in hindi for emotion recognition in dialogues. In Proceedings of the Thirteenth Language Resources and Evaluation Conference. 5829–5837.

[112]

Youngseo Son, Nipun Bayas, and H. Andrew Schwartz. 2018. Causal explanation analysis on social media. arXiv preprint arXiv:1809.01202 (2018).

[113]

Hoyun Song, Jinseon You, Jin-Woo Chung, and Jong C. Park. 2018. Feature attention network: Interpretable depression detection from social media. In Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation.

[114]

Xuemeng Song, Liqiang Nie, Luming Zhang, Mohammad Akbari, and Tat-Seng Chua. 2015. Multiple social network learning and its application in volunteerism tendency prediction. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 213–222.

Digital Library

[115]

Maxim Stankevich, Vadim Isakov, Dmitry Devyatkin, and Ivan Smirnov. 2018. Feature engineering for depression detection in social media. In Proceedings of the ICPRAM. 426–431.

[116]

Hao Sun, Zhenru Lin, Chujie Zheng, Siyang Liu, and Minlie Huang. 2021. Psyqa: A chinese dataset for generating long counseling text for mental health support. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021). 1489–1503.

[117]

Rizma Adlia Syakurah, Violantina Linardi, and Ilsyafitri Bonita. 2021. COVID-19 infodemic and Indonesian emotional and mental health state. International Journal of Public Health 10, 4 (2021), 927–933.

[118]

Michael M. Tadesse, Hongfei Lin, Bo Xu, and Liang Yang. 2019. Detection of depression-related posts in reddit social media forum. IEEE Access 7 (2019), 44883–44893.

[119]

Michael Mesfin Tadesse, Hongfei Lin, Bo Xu, and Liang Yang. 2020. Detection of suicide ideation in social media forums using deep learning. Algorithms 13, 1 (2020), 7.

[120]

Ilija Tavchioski, Boshko Koloski, Blaž Škrlj, and Senja Pollak. 2022. E8-IJS@ LT-EDI-ACL2022-BERT, AutoML and knowledge-graph backed detection of depression. In Proceedings of the 2nd Workshop on Language Technology for Equality, Diversity and Inclusion. 251–257.

[121]

Elsbeth Turcan and Kathleen McKeown. 2019. Dreaddit: A reddit dataset for stress analysis in social media. In Proceedings of the 10th International Workshop on Health Text Mining and Information Analysis (LOUHI’19). 97–107.

[122]

Elsbeth Turcan, Smaranda Muresan, and Kathleen McKeown. 2021. Emotion-infused models for explainable psychological stress detection. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2895–2909.

[123]

Vajratiya Vajrobol, Unmesh Shukla, Amit Pundir, Sanjeev Singh, and Geetika Jain Saxena. 2022. Depression detection in Thai language posts based on attentive network models. (2022).

[124]

James Vincent. 2017. Facebook is using AI to spot users with suicidal thoughts and send them help. The Verge (2017).

[125]

Mélanie Karp Claus Vögele. 2016. Does anyone still understand me? psychotherapy and multilingualism. Verhaltenstherapie 26 (2016), 156–157.

[126]

Xin Wang, Lei Cao, Huijun Zhang, Ling Feng, Yang Ding, and Ningyun Li. 2022. A meta-learning based stress category detection framework on social media. In Proceedings of the ACM Web Conference 2022. 2925–2935.

Digital Library

[127]

Zhentao Xu, Verónica Pérez-Rosas, and Rada Mihalcea. 2020. Inferring social media users’s mental health status from multimodal information. In Proceedings of the 12th Language Resources and Evaluation Conference. 6292–6299.

[128]

Kailai Yang, Tianlin Zhang, and Sophia Ananiadou. 2022. A mental state Knowledge–aware and Contrastive Network for early stress and depression detection on social media. Information Processing and Management 59, 4 (2022), 102961.

Digital Library

[129]

Liang Yang, Shuqun Li, Xi Luo, Bo Xu, Yuanling Geng, Zeyuan Zeng, Fan Zhang, and Hongfei Lin. 2022. Computational personality: A survey. Soft Computing (2022), 1–19.

[130]

Andrew Yates, Arman Cohan, and Nazli Goharian. 2017. Depression and self-harm risk assessment in online forums. In Proceedings of the EMNLP.

[131]

Lixia Yu, Wanyue Jiang, Zhihong Ren, Sheng Xu, Lin Zhang, and Xiangen Hu. 2021. Detecting changes in attitudes toward depression on Chinese social media: A text analysis. Journal of Affective Disorders 280 (2021), 354–363.

[132]

Rida Zainab and Rajarathnam Chandramouli. 2020. Detecting and explaining depression in social media text with machine learning. In Proceedings of the GOOD Workshop KDD, Vol. 20.

[133]

Sourabh Zanwar, Xiaofei Li, Daniel Wiechmann, Yu Qiao, and Elma Kerz. 2023. What to fuse and how to fuse: Exploring emotion and personality fusion strategies for explainable mental disorder detection. In Findings of the Association for Computational Linguistics: ACL 2023. 8926–8940.

[134]

Sicheng Zhao, Shangfei Wang, Mohammad Soleymani, Dhiraj Joshi, and Qiang Ji. 2019. Affective computing for large-scale heterogeneous multimedia data: A survey. ACM Transactions on Multimedia Computing, Communications, and Applications 15, 3s (2019), 1–32.

Digital Library

[135]

Yun Zhao, Dexi Liu, Changxuan Wan, Xiping Liu, Xiangqing Qiu, and Jianyun Nie. 2022. Find supports for the post about mental issues: More than semantic matching. Transactions on Asian and Low-Resource Language Information Processing 21, 6 (2022), 1–14.

Digital Library

[136]

Hamad Zogan, Imran Razzak, Shoaib Jameel, and Guandong Xu. 2021. DepressionNet: A novel summarization boosted deep framework for depression detection on social media. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (Association for Computing Machinery), 133–142.

[137]

Hamad Zogan, Imran Razzak, Xianzhi Wang, Shoaib Jameel, and Guandong Xu. 2022. Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media. World Wide Web (2022), 1–24.

[138]

Hamad Zogan, Xianzhi Wang, Shoaib Jameel, and Guandong Xu. 2020. Depression detection with multi-modalities using a hybrid deep learning model on social media. World Wide Web (2022), 1–24.

Cited By

Gavhane JPrasad RJadhav AParashar S(2024)Natural Language Processing for Emotion Recognition and AnalysisAffective Computing for Social Good10.1007/978-3-031-63821-3_6(107-128)Online publication date: 8-Oct-2024
https://doi.org/10.1007/978-3-031-63821-3_6
Mishra DDeshpande SAnna MTiwari A(2024)Exploring the Ethical Dimensions and Societal Consequences of Affective ComputingAffective Computing for Social Good10.1007/978-3-031-63821-3_5(91-105)Online publication date: 8-Oct-2024
https://doi.org/10.1007/978-3-031-63821-3_5
Lokhande HGarware CKudale TKumar R(2024)Personalized Well-Being Interventions (PWIs): A New Frontier in Mental HealthAffective Computing for Social Good10.1007/978-3-031-63821-3_10(183-200)Online publication date: 8-Oct-2024
https://doi.org/10.1007/978-3-031-63821-3_10

Index Terms

Towards Mental Health Analysis in Social Media for Low-resourced Languages
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing design and evaluation methods
      1. Social network analysis
    2. Collaborative and social computing theory, concepts and paradigms
      1. Social engineering (social sciences)
      2. Social media
2. Information systems
  1. Information systems applications
    1. Data mining
  2. World Wide Web
    1. Web applications
      1. Social networks
    2. Web searching and information discovery
      1. Personalization

Recommendations

Detecting and Characterizing Mental Health Related Self-Disclosure in Social Media
CHI EA '15: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems

Self-disclosure is an important element facilitating improved psychological wellbeing in individuals with mental illness. As social media is increasingly adopted in health related discourse, we examine how these new platforms might be allowing honest ...
Mental Health Computing via Harvesting Social Media Data
ASMMC-MMAC'18: Proceedings of the Joint Workshop of the 4th Workshop on Affective Social Multimedia Computing and first Multi-Modal Affective Computing of Large-Scale Multimedia Data

Psychological stress and depression are threatening people's health. It is non-trivial to detect stress or depression timely for proactive care. With the popularity of social media, people are used to sharing their daily activities and interacting with ...
Do socially anxious and non-anxious individuals differ in their social media use?
Abstract
Numerous studies have shown that socially anxious individuals prefer technology-mediated communication over face-to-face interactions. The aim of this study was to examine possible differences between socially anxious and non-anxious individuals ...
Highlights
- Socially anxious individuals do not differ from non-socially anxious individuals in time spent on social media
- Socially anxious individuals are characterized by less active social media use compared to non-socially anxious individuals

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 23, Issue 3

March 2024

277 pages

EISSN:2375-4702

DOI:10.1145/3613569

Editor:
Imed Zitouni
Google, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 March 2024

Online AM: 30 December 2023

Accepted: 16 December 2023

Revised: 12 September 2023

Received: 01 November 2022

Published in TALLIP Volume 23, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
517
Total Downloads

Downloads (Last 12 months)517
Downloads (Last 6 weeks)69

Reflects downloads up to 04 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Gavhane JPrasad RJadhav AParashar S(2024)Natural Language Processing for Emotion Recognition and AnalysisAffective Computing for Social Good10.1007/978-3-031-63821-3_6(107-128)Online publication date: 8-Oct-2024
https://doi.org/10.1007/978-3-031-63821-3_6
Mishra DDeshpande SAnna MTiwari A(2024)Exploring the Ethical Dimensions and Societal Consequences of Affective ComputingAffective Computing for Social Good10.1007/978-3-031-63821-3_5(91-105)Online publication date: 8-Oct-2024
https://doi.org/10.1007/978-3-031-63821-3_5
Lokhande HGarware CKudale TKumar R(2024)Personalized Well-Being Interventions (PWIs): A New Frontier in Mental HealthAffective Computing for Social Good10.1007/978-3-031-63821-3_10(183-200)Online publication date: 8-Oct-2024
https://doi.org/10.1007/978-3-031-63821-3_10

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents