Act or Wait and See the Challenges: Artificial Intelligence for Analysing Schizophrenia Syndromes in Social Media

Akash  Gulati; Sugandh  Arora; Kirti Dang  Longani

doi:10.21070/pssh.v7i.621

(1) Akash Gulati ICFAI Business School Hyderabad
India
(2) Sugandh Arora Ajeenkya DY Patil University
India
(3) * Kirti Dang Longani Ajeenkya DY Patil University
India
(*) Corresponding Author

DOI: https://doi.org/10.21070/pssh.v7i.621

Abstract

Schizophrenia is a complicated and crippling mental illness that makes it hard to find and treat early on. With the rise of social media, there is a lot of information available to help people learn more about mental health issues, such as schizophrenia syndromes. It might be possible to find possible signs of schizophrenia and improve the diagnostic process by using artificial intelligence (AI) techniques to look at social media data. The frequent use of social media can be indicative of linguistic impairments or alterations brought on by symptoms shared by a variety of mental health illnesses. Over the past 25 years, the detection of these linguistic cues has been studied; however, with the pandemic, interest and methodological advancement have increased dramatically. It is possible that within the next ten years, trustworthy techniques for utilising social media data to forecast mental health status will emerge. This could have an impact on public health policy and clinical practise, especially when it comes to early intervention in mental health treatment.

References

Ammari, T., & Schoenebeck, S. (2015). Networked empowerment on Facebook groups for parents of children with special needs. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 2805-2814.

Amir, S., Coppersmith, G., Carvalho, P., Silva, M. J., & Wallace, B. C. (2017). Quantifying mental health from social media with neural user embeddings. Machine Learning for Healthcare Conference, 306-321.

Andalibi, N., Ozturk, P., & Forte, A. (2017). Sensitive self-disclosures, responses, and social support on Instagram: The case of #depression. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, 1485-1500.

Birnbaum, M. L., Ernala, S. K., Rizvi, A. F., Arenare, E., Van Meter, A. R., De Choudhury, M., & Kane, J. M. (2019). Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook. NPJ Schizophrenia, 5(1), 17. https://doi.org/10.1038/s41537-019-0085-9.

Bucur, A. M., & Cosma, A. C. (2023). Automatic detection and classification of mental illnesses from general social media texts. Proceedings of the International Conference on Recent Advances in Natural Language Processing, 358-366.

Cai, N., Revez, J. A., Adams, M. J., Andlauer, T. F. M., Breen, G., Byrne, E. M., Clarke, T. K., Forstner, A. J., Grabe, H. J., Hamilton, S. P., Levinson, D. F., Lewis, C. M., Lewis, G., Martin, N. G., Milaneschi, Y., Mors, O., Müller-Myhsok, B., Penninx, B. W. J. H., Perlis, R. H., ... Kendler, K. S. (2020). Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nature Genetics, 52(4), 437-447. https://doi.org/10.1038/s41588-020-0594-5

Chancellor, S., Baumer, E. P., & De Choudhury, M. (2019). Who is the "human" in human-centered machine learning: The case of predicting mental health from social media. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1-32.

Chen, Z., Yang, R., Fu, S., Zong, N., Liu, H., & Huang, M. (2023). Detecting Reddit users with depression using a hybrid neural network SBERT-CNN. Proceedings of the 11th International Conference on Healthcare Informatics, 193-199. https://doi.org/10.1109/ICHI2023.193199.

Cohan, A., Desmet, B., Yates, A., Soldaini, L., MacAvaney, S., & Goharian, N. (2018). SMHD: A large-scale resource for exploring online language usage for multiple mental health conditions. Proceedings of the 27th International Conference on Computational Linguistics, 1485-1497.

Cong, Q., Feng, Z., Li, F., Xiang, Y., Rao, G., & Tao, C. (2018). XA-BiLSTM: A deep learning approach for depression detection in imbalanced data. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1624-1627.

Coppersmith, G., Dredze, M., & Harman, C. (2014). Quantifying mental health signals in Twitter. Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, 51-60.

De Choudhury, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. Proceedings of the International AAAI Conference on Web and Social Media, 7(1), 128-137.

De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Social media as a measurement tool of depression in populations. Proceedings of the 5th Annual ACM Web Science Conference, 47-56.

Dinu, A., & Moldovan, A. C. (2021). Automatic detection and classification of mental illnesses from general social media texts. Proceedings of the International Conference on Recent Advances in Natural Language Processing, 358-366.

Eichstaedt, J. C., Smith, R. J., Merchant, R. M., Ungar, L. H., Crutchley, P., Preoţiuc-Pietro, D., Asch, D. A., & Schwartz, H. A. (2018). Facebook language predicts depression in medical records. Proceedings of the National Academy of Sciences, 115(44), 11203-11208. https://doi.org/10.1073/pnas.1802331115

Ford, E., Curlewis, K., Wongkoblap, A., & Curcin, V. (2019). Public opinions on using social media content to identify users with depression and target mental health care advertising: Mixed methods survey. JMIR Mental Health, 6(11), e12942. https://doi.org/10.2196/12942

Doktoronline. (2024, February 29). Klikk.no. https://www.klikk.no/helse/doktoronline/

Fujita, F., Diener, E., & Sandvik, E. (1991). Gender differences in negative affect and well-being: The case for emotional intensity. Journal of Personality and Social Psychology, 61(3), 427-434. https://doi.org/10.1037//0022-3514.61.3.427.

Ganguly, C., Nayak, S., & Gupta, A. K. (2022). Mental health impact of COVID-19 and machine learning applications in combating mental disorders: A review. Artificial Intelligence, Machine Learning, and Mental Health in Pandemics, 1-51.

GBD 2019 Mental Disorders Collaborators. (2022). Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry, 9(2), 137-150. https://doi.org/10.1016/S2215-0366(21)00395-3.

Golder, S., Ahmed, S., Norman, G., & Booth, A. (2017). Attitudes toward the ethics of research using social media: A systematic review. Journal of Medical Internet Research, 19(6), e195. https://doi.org/10.2196/jmir.7082

Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261-266. https://doi.org/10.1126/science.aaa8685

Holmes, E. A., O'Connor, R. C., Perry, V. H., Tracey, I., Wessely, S., Arseneault, L., & Bullmore, E. (2020). Multidisciplinary research priorities for the COVID-19 pandemic: A call for action for mental health science. Lancet Psychiatry, 7(6), 547-560. https://doi.org/10.1016/S2215-0366(20)30168-1.

Hruska, J., & Maresova, P. (2020). Use of social media platforms among adults in the United States—Behavior on social media. Societies, 10(1), 27. https://doi.org/10.3390/soc10010027

MacAvaney, S., Desmet, B., Cohan, A., Soldaini, L., Yates, A., Zirikly, A., & Goharian, N. (2018). RSDD-Time: Temporal annotation of self-reported mental health diagnoses. Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, 168-173.

Johnsen, J. A., Rosenvinge, J. H., & Gammon, D. (2002). Online group interaction and mental health: An analysis of three online discussion forums. Scandinavian Journal of Psychology, 43(5), 445-449. https://doi.org/10.1111/1467-9450.00313

Kelley, S. W., & Gillan, C. M. (2022). Using language in social media posts to study the network dynamics of depression longitudinally. Nature Communications, 13(1), 870. https://doi.org/10.1038/s41467-022-28513-3

Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., Rush, A. J., Walters, E. E., & Wang, P. S. (2003). The epidemiology of major depressive disorder: Results from the National Comorbidity Survey Replication (NCS-R). JAMA, 289(23), 3095-3105. https://doi.org/10.1001/jama.289.23.3095

Kulkarni, H., MacAvaney, S., Goharian, N., & Frieder, O. (2023). Knowledge augmentation for early depression detection. Proceedings of the International Workshop on Health Intelligence, 175-191.

Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019). Albert: A lite BERT for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942. https://doi.org/10.48550/arXiv.1909.11942

Li, A., Jiao, D., Liu, X., & Zhu, T. (2020). A comparison of the psycholinguistic styles of schizophrenia-related stigma and depression-related stigma on social media: Content analysis. Journal of Medical Internet Research, 22(4), e16470. https://doi.org/10.2196/16470

Liu, G., Wang, C., Peng, K., Huang, H., & Cheng, W. (2019). Socinf: Membership inference attacks on social media health data with machine learning. Transactions on Computational Social Systems, 6(5), 907-921.

Mikal, J. P., Hurst, S., & Conway, M. (2016). Ethical issues in using Twitter for population-level depression monitoring: A qualitative study. BMC Medical Ethics, 17(1), 22. https://doi.org/10.1186/s12910-016-0105-5

Moreno, M. A., Jelenchick, L. A., Egan, K. G., Cox, E., Young, H., Gannon, K. E., & Becker, T. (2011). Feeling bad on Facebook: Depression disclosures by college students on a social networking site. Depression and Anxiety, 28(6), 447-455. https://doi.org/10.1002/da.20805

Muhammad, K. A. (2023). Unveiling the emotional and psychological states of Instagram users: A deep learning approach to mental health analysis. Information Sciences Letters, 12(5).

Nicholas, J., Onie, S., & Larsen, M. E. (2020). Ethics and privacy in social media research for mental health. Current Psychiatry Reports, 22(12), 84. https://doi.org/10.1007/s11920-020-01205-9

Owen, D., Antypas, D., Hassoulas, A., Pardiñas, A. F., Espinosa-Anke, L., & Collados, J. C. (2023). Enabling early healthcare intervention by detecting depression in users of web-based forums using language models: Longitudinal analysis and evaluation. JMIR AI, 2, e41205. https://doi.org/10.2196/41205.

Picardi, A., Lega, I., Tarsitani, L., Caredda, M., Matteucci, G., Zerella, M. P., & Biondi, M. (2016). A randomized controlled trial of the effectiveness of a program for early detection and treatment of depression in primary care. Journal of Affective Disorders, 198, 96-101. https://doi.org/10.1016/j.jad.2016.03.025.

Reece, A. G., Reagan, A. J., Lix, K. L. M., Dodds, P. S., Danforth, C. M., & Langer, E. J. (2017). Forecasting the onset and course of mental illness with Twitter data. Scientific Reports, 7(1), 13006. https://doi.org/10.1038/s41598-017-12961-9

Resnik, P., Armstrong, W., Claudino, L., Nguyen, T., Nguyen, V. A., & Boyd-Graber, J. (2015). Beyond LDA: Exploring supervised topic modeling for depression-related language in Twitter. Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, 99-107.

Seabrook, E. M., Kern, M. L., Fulcher, B. D., & Rickard, N. S. (2018). Predicting depression from language-based emotion dynamics: Longitudinal analysis of Facebook and Twitter status updates. Journal of Medical Internet Research, 20(5), e168. https://doi.org/10.2196/jmir.9267

Singh, A., & Singh, J. (2021). Automation of detection of social network mental disorders–A review. IOP Conference Series: Materials Science and Engineering, 1022(1), 012008. https://doi.org/10.1088/1757-899X/1022/1/012008.

Szeto, M. D., Barber, C., Ranpariya, V. K., Anderson, J., Hatch, J., Ward, J., & Coolman, T. (2022). Emojis and emoticons in healthcare and dermatology communication: Narrative review. JMIR Dermatology, 5(3), e33851. https://doi.org/10.2196/33851.

Tadesse, M. M., Lin, H., Xu, B., & Yang, L. (2019). Detection of depression-related posts in Reddit social media forum. Access, 7, 44883-44893.

Thorn, P., La Sala, L., Hetrick, S., Rice, S., Lamblin, M., & Robinson, J. (2023). Motivations and perceived harms and benefits of online communication about self-harm: An interview study with young people. Digital Health, 9, 20552076231176689. https://doi.org/10.1177/20552076231176689.

Yates, A., Cohan, A., & Goharian, N. (2017). Depression and self-harm risk assessment in online forums. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2968-2978.

Zhang, Y., Lyu, H., Liu, Y., Zhang, X., Wang, Y., & Luo, J. (2021). Monitoring depression trends on Twitter during the COVID-19 pandemic: Observational study. JMIR Infodemiology, 1(1), e26769. https://doi.org/10.2196/26769.