Abstract
Depression is a major public health concern that currently lacks reliable biomarkers for diagnosis and assessment. This study aims to analyse linguistic patterns associated with depressive symptoms during interactions with Virtual Humans (VH). 40 participants were equally divided into a control group and a group exhibiting depressive symptoms. Each participant engaged in six semi-guided conversations with VHs designed to elicit basic emotions and powered by a Large Language Model to generate automatic responses. Participants’ speech was transcribed and linguistic features were extracted using LIWC. Non-parametric statistical tests were employed to examine differences between groups. Participants with depressive symptoms used more words associated with negation and exclusion, and referred more to negative emotions. Control participants based their speech more on certainty and temporal reference, and used more frequently words related to friendships and health. The results support the potential of VHs and linguistic patterns as valid biomarkers for assessing depression.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dattani, S., Ritchie, H., Roser, M.: Mental health. Our World in Data (2021). https://ourworldindata.org/mental-health
Lakkis, N.A., Mahmassani, D.M.: Screening instruments for depression in primary care: a concise review for clinicians. Postgraduate Med. 127(1), 99–106 (2015)
Ahmedani, B.K., et al.: Health care contacts in the year before suicide death. J. General Internal Med. 29, 870–877 (2014)
Cusin, C., Yang, H., Yeung, A., Fava, M.: Rating scales for depression. Handbook of Clinical Rating Scales and Assessment in Psychiatry and Mental Health, pp. 7–35 (2010)
Grimm, P.: Social desirability bias. Wiley International Encyclopedia of Marketing (2010)
Kosorok, M.R., Laber, E.B.: Precision medicine. Annu. Rev. Stat. Appl. 6, 263–286 (2019)
Huys, Q.J.M., Maia, T.V., Frank, M.J.: Computational psychiatry as a bridge from neuroscience to clinical applications. Nat. Neurosci. 19(3), 404–413 (2016)
Gold, J.M., et al.: Negative symptoms and the failure to represent the expected reward value of actions: behavioral and computational modeling evidence. Arch. General Psychiatry 69(2), 129–138 (2012)
Strauss, G.P., Gold, J.M.: A new perspective on anhedonia in schizophrenia. Am. J. Psychiatry 169(4), 364–373 (2012)
Dev, A., et al.: Exploration of EEG-based depression biomarkers identification techniques and their applications: a systematic review. IEEE Access (2022)
Schiweck, C., Piette, D., Berckmans, D., Claes, S., Vrieze, E.: Heart rate and high frequency heart rate variability during stress as biomarker for clinical depression. a systematic review. Psychol. Med. 49(2), 200–211 (2019)
Sarchiapone, M., et al.: The association between electrodermal activity (EDA), depression and suicidal behaviour: A systematic review and narrative synthesis. BMC Psychiatry 18(1), 2018. Cited by: 81; All Open Access, Gold Open Access, Green Open Access
Mahmoud, M., Robinson, P.: Towards automatic analysis of gestures and body expressions in depression. volume 2016-January, pp. 276–277 (2016)
Smrke, U., Mlakar, I., Lin, S., Musil, B., Plohl, N.: Language, speech, and facial expression features for artificial intelligence–based detection of cancer survivors’ depression: scoping meta-review. JMIR Mental Health 8(12) (2021). Cited by: 6; All Open Access, Gold Open Access, Green Open Access
Cummins, N., Scherer, S., Krajewski, J., Schnieder, S., Epps, J., Quatieri, T.F.: A review of depression and suicide risk assessment using speech analysis. Speech Commun. 71, 10–49 (2015). Cited by: 462
Munthuli, A., et al.: Classification and analysis of text transcription from Thai depression assessment tasks among patients with depression. PLoS ONE 18(3), e0283095 (2023)
Babu, N. V., Kanaga, E.G.M.: Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN Comput. Sci. 3, 1–20 (2022)
Fang, C., Dianatobing, G., Atara, T., Edbert, I.S., Suhartono. D.: Feature extraction methods for depression detection through social media text. In: 2022 6th International Conference on Informatics and Computational Sciences (ICICoS), pp. 117–121 (2022)
Lyu, S., Ren, X., Du, Y., Zhao, N.: Detecting depression of Chinese microblog users via text analysis: Combining linguistic inquiry word count (LIWC) with culture and suicide related lexicons. Front. Psychiatry 14 (2023)
Landoni, M., et al.: Linguistic features of postpartum depression using linguistic inquiry and word count text analysis. J. Neonatal Nurs. 29(1), 127–134 (2023)
Stamatis, C.A., et al.: Prospective associations of text-message-based sentiment with symptoms of depression, generalized anxiety, and social anxiety. Depress. Anxiety 39(12), 794–804 (2022). Cited by: 0; All Open Access, Bronze Open Access, Green Open Access
Salas-Zárate, R., et al.: Detecting depression signs on social media: a systematic literature review. Healthcare 10(2) (2022)
Marín-Morales, J., et al.: Affective computing in virtual reality: emotion recognition from brain and heart-beat dynamics using wearable sensors. Sci. Rep. 8(1), 13657 (2018)
He, L., et al.: Deep learning for depression recognition with audiovisual cues: a review. Inf. Fusion 80, 56–86 (2022)
Chitale, V., et al.: The use of videogames and virtual reality for the assessment of anxiety and depression: a scoping review. Games Health J. 11(6), 341–354 (2022)
DeVault, D., et al.: Simsensei kiosk: a virtual human interviewer for healthcare decision support. In Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems, pp. 1061–1068 (2014)
Burden, D., Savin-Baden, M.: Virtual Humans: Today and Tomorrow. CRC Press (2019)
Girard, J.M., Cohn, J.F., Mahoor, M.H., Mavadati, S.M., Hammal, Z., Rosenwald, D.P.: Nonverbal social withdrawal in depression: evidence from manual and automatic analyses. Image Vision Comput. 32(10), 641–647 (2014)
Jens Tyge MØrk Schelde: Major depression: Behavioral markers of depression and recovery. J. Nerv. Ment. Dis. 186(3), 133–140 (1998)
LeMoult, J., Gotlib, I.H.: Depression: a cognitive perspective. Clin. Psychol. Rev. 69, 51–66 (2019). Advances in cognitive science and psychopathology
Orth, U., Robins, R.W.: Understanding the link between low self-esteem and depression. Current Direct. Psychol. Sci. 22(6), 455–460 (2013)
Gaynes, B.N., Burns, B.J., Tweed, D.L., Erickson, P.: Depression and health-related quality of life. J. Nerv. Mental Dis. 190(12), 799–806 (2002)
Egede, L.E., Ellis, C., Grubaugh, A.L.: The effect of depression on self-care behaviors and quality of care in a national sample of adults with diabetes. Gen. Hosp. Psychiatry 31(5), 422–427 (2009)
Acknowledgements
This work was supported by the Generalitat Valenciana, Spain (ACIF/2021/187) and by the Vicerrectorado de Investigación de la Universitat Politècnica de València (UPV), Spain [Ayuda a Primeros Proyectos de Investigación (PAID-06–22); (PAID-10–20)].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 IFIP International Federation for Information Processing
About this paper
Cite this paper
Gómez-Zaragozá, L., Minissi, M.E., Llanes-Jurado, J., Altozano, A., Alcañiz Raya, M., Marín-Morales, J. (2023). Linguistic Indicators of Depressive Symptoms in Conversations with Virtual Humans. In: Camarinha-Matos, L.M., Boucher, X., Ortiz, A. (eds) Collaborative Networks in Digitalization and Society 5.0. PRO-VE 2023. IFIP Advances in Information and Communication Technology, vol 688. Springer, Cham. https://doi.org/10.1007/978-3-031-42622-3_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-42622-3_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42621-6
Online ISBN: 978-3-031-42622-3
eBook Packages: Computer ScienceComputer Science (R0)
