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Detection of Postpartum Depression-Related Posts: An Analysis for Serbian

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Intelligent Distributed Computing XIV (IDC 2021)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1026))

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Abstract

This paper presents a model of an intelligent distributed support system for pregnant women and mothers with an emphasis on the detection of postpartum depression. There are psychological tests that can be used to determine the degree of postpartum depression, but it is very important to detect the existence of this depression in women who are not aware of it. A lot of women use social networks to share information about their problems. Using tools for natural language processing and machine learning, social network posts that describing problems similar to postpartum depression can be detected. The presented system was tested on a data set of posts from two social media forums (one Serbian and one English). It has been shown that posts indicating postpartum depression can be labeled with an accuracy of up to 89%. This system aims to identify the potential existence of problems and refer to the system for detection and professional assistance. It can contribute to improving the lives of mothers and thus babies.

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References

  1. Cox, J. L., Holden, J. M., Sagovsky, R.: Detection of postnatal depression. Development of the 10-item edinburgh postnatal depression scale. Br. J. Psychiatr. 150:782–786 (1987). https://doi.org/10.1192/bjp.150.6.782.PMID:3651732

  2. Amazon Mechanical Turk. https://www.mturk.com/. Accessed 15 July 2021

  3. Quora (2021). https://www.quora.com/. Accessed 15 July 2021

  4. Topcoder (2021). http://topcoder.com/. Accessed 15 July 2021

  5. Giuntini, F.T., Cazzolato, M.T., dos Reis, M.D.J.D., Campbell, A.T., Traina, A.J., Ueyama, J.: A review on recognizing depression in social networks: challenges and opportunities. J. Ambient Intell. Hum. Comput. 11(11), 4713–4729 (2020)

    Article  Google Scholar 

  6. Coppersmith, G., Dredze, M., Harman, C., Hollingshead, K. : From ADHD to SAD: Analyzing the language of mental health on Twitter through self-reported diagnoses. In: Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: from Linguistic Signal to Clinical Reality, pp. 1–10 (2015)

    Google Scholar 

  7. Islam, M.R., Kabir, M.A., Ahmed, A., Kamal, A.R.M., Wang, H., Ulhaq, A.: Depression detection from social network data using machine learning techniques. Health Inf. Sci. Syst. 6(1), 1–12 (2018)

    Article  Google Scholar 

  8. De Choudhury, M., Counts, S., Horvitz, E.: Predicting postpartum changes in emotion and behavior via social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3267–3276 (2013)

    Google Scholar 

  9. Shatte, A.B.R., Hutchinson, D.M., Fuller-Tyszkiewicz, M., Teague, S.J.: Social Media Markers to Identify Fathers at Risk of Postpartum Depression: A Machine Learning Approach, Cyberpsychology, Behavior, and Social Networking (2020). https://doi.org/10.1089/cyber.2019.074

  10. A., Semeraro, D., Drake, J., Bukowski, R., Oliveira, J.L.: Social media mining for postpartum depression prediction. In: Digital Personalized Health and Medicine, pp. 1391–1392. IOS Press (2020)

    Google Scholar 

  11. Marovac, U., Ljajić, A., Avdić, A., Fazlagić, A.: Automation of psychological testing of stressful situations in the Serbian, ICIST. In: 2019 Proceedings, pp. 102–106 (2019)

    Google Scholar 

  12. Reddit (2021). https://www.reddit.com/. Accessed 15 July 2021

  13. Ana.rs (2021). https://www.ana.rs/forum/. Accessed 15 Jul 2021

  14. Translator (2021). https://api.cognitive.microsofttranslator.com. Accessed 15 July 2021

  15. Tadesse, M.M., Lin, H., Xu, B., Yang, L.: Detection of depression-related posts in reddit social media forum. IEEE Access 7, 44883–44893 (2019)

    Article  Google Scholar 

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Acknowledgements

This paper is partially supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia under project III44007.

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Correspondence to Ulfeta Marovac .

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Marovac, U., Avdić, A. (2022). Detection of Postpartum Depression-Related Posts: An Analysis for Serbian. In: Camacho, D., Rosaci, D., Sarné, G.M.L., Versaci, M. (eds) Intelligent Distributed Computing XIV. IDC 2021. Studies in Computational Intelligence, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-96627-0_34

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