Pemodelan Topik Risiko Bunuh Diri Berdasarkan Konten Media Sosial dengan Latent Dirichlet Allocation
Kata Kunci:
pemodelan topik, risiko bunuh diri, latent dirichlet allocation, topic coherence, redditAbstrak
Bunuh diri merupakan masalah kesehatan yang serius. Tidak hanya menyebabkan hilangnya nyawa secara sia-sia, bunuh diri juga dapat meninggalkan dampak yang berkepanjangan bagi mereka yang ditinggalkan. Meskipun begitu, stigma dan kekhawatiran akan perlakuan diskriminatif masih menjadi penghambat dalam upaya pencegahan bunuh diri. Mereka yang memiliki pemikiran bunuh diri cenderung memilih media sosial sebagai tempat bercerita. Pemahaman terhadap topik yang mereka bicarakan dapat menjadi salah satu langkah dalam peningkatan upaya pencegahan bunuh diri. Oleh karena itu, pada penelitian ini dilakukan pemodelan topik menggunakan metode Latent Dirichlet Allocation untuk mendapatkan gambaran mengenai topik yang dibicarakan dalam subreddit r/SuicideWatch. Pengujian terhadap pemodelan topik yang dilakukan menghasilkan nilai coherence tertinggi sebesar 0,2947. Nilai tersebut diperoleh menggunakan parameter α = 1/T, β = 1/T, dan T = 9. Walaupun memiliki nilai coherence tertinggi dibanding pengujian lain, pengujian tersebut menghasilkan topik yang sulit diinterpretasi karena banyaknya kata umum yang muncul. Pengujian lain yang menggunakan parameter α = 50/T, β = 1/T, dan T = 5 memberikan nilai coherence yang lebih rendah, tetapi topik yang dihasilkan lebih mudah untuk diinterpretasi. Beberapa topik yang dihasilkan, antara lain, rasa ketidakberdayaan, kondisi emosional, hubungan sosial, serta pemikiran atau perencanaan bunuh diri.
Referensi
Anandarajan, M., Hill, C., & Nolan, T., 2019. Practical Text Analytics: Maximizing the Value of Text Data. Cham: Springer Nature.
Blei, D.M., Ng, A.Y. & Jordan, M.I., 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research, [online] Tersedia di: <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>
Chatterjee, M., Kumar, P., Samanta, P. & Sarkar, D., 2022. Suicide Ideation Detection from Online Social Media: A Multi-Modal Feature Based Technique. International Journal of Information Management Data Insights, 2(2), p.100103.
George, C.P. & Doss, H., 2018. Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model. Journal of Machine Learning Research, [online] Tersedia di: <https://www.jmlr.org/papers/v18/15-595.html>
Hamdan, S., Berkman, N., Lavi, N., Levy, S. & Brent, D., 2019. The Effect of Sudden Death Bereavement on the Risk for Suicide. Crisis.
Kamarudin, N.S., Beigi, G. & Liu, H., 2021. A Study on Mental Health Discussion through Reddit. In: 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM). Pekan, Malaysia, 24-26 August 2021. IEEE.
Kim, W.J., Song, Y.J., Ryu, H.S., Ryu, V., Kim, J.M., Ha, R.Y., Lee, S.J., Namkoong, K., Ha, K. & Cho, H.S., 2015. Internalized Stigma and Its Psychosocial Correlates in Korean Patients with Serious Mental Illness. Psychiatry research, 225(3), pp.433-439.
Mardones-Segovia, C., Wheeler, J.M., Choi, H.J., Wang, S. & Cohen, A.S., 2023. Model Selection for Latent Dirichlet Allocation in Assessment Data. Psychological Test and Assessment Modeling, 65(1), pp.3-35.
Putri, I.R. & Kusumaningrum, R., 2017. Latent Dirichlet allocation (LDA) for Sentiment Analysis toward Tourism Review in Indonesia. Journal of Physics: Conference Series, [online] Tersedia di: <https://iopscience.iop.org/article/10.1088/1742-6596/801/1/012073/meta>
Rabani, S.T., Khan, Q.R. & Khanday, A.M.U.D., 2020. Detection of Suicidal Ideation on Twitter using Machine Learning & Ensemble Approaches. Baghdad science journal, 17(4), pp.1328-1328.
Röder, M., Both, A. & Hinneburg, A., 2015. Exploring the Space of Topic Coherence Measures. In: Proceedings of the eighth ACM international conference on Web search and data mining. Shanghai, China, 2-6 February 2015. New York: ACM.
Sievert, C. & Shirley, K., 2014. LDAvis: A Method for Visualizing and Interpreting Topics. In: Proceedings of the workshop on interactive language learning, visualization, and interfaces. Maryland, USA, 27 June 2014. ACL.
Sik, D., Németh, R. & Katona, E., 2021. Topic Modelling Online Depression Forums: Beyond Narratives of Self-Objectification and Self-Blaming. Journal of Mental Health, 32(2), pp.386-395.
Song, H., You, J., Chung, J.W. & Park, J.C., 2018. Feature Attention Network: Interpretable Depression Detection from social Media. Proceedings of the 32nd Pacific Asia conference on language, information and computation. Hong Kong, 1-3 December 2018. ACL.
Syed, S. & Spruit, M., 2017. Full-Text or Abstract? Examining Topic Coherence Scores using Latent Dirichlet Allocation. In: 2017 IEEE International conference on data science and advanced analytics (DSAA). Tokyo, Japan, 19-21 October 2017. IEEE.
Tadesse, M.M., Lin, H., Xu, B. & Yang, L., 2019. Detection of Depression-Related Posts in Reddit Social Media Forum. IEEE Access, 7, pp.44883-44893.
Turcan, E., Muresan, S. & McKeown, K., 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. 6–11 June 2021. ACL.
Wagner, B., Hofmann, L. & Grafiadeli, R., 2021. The Relationship Between Guilt, Depression, Prolonged grief, and Posttraumatic Stress Symptoms After Suicide Bereavement. Journal of Clinical Psychology, 77(11), pp.2545-2558.
WHO, 2023. Suicide. [online] Tersedia di: <https://www.who.int/news-room/fact-sheets/detail/suicide>
Anandarajan, M., Hill, C., & Nolan, T., 2019. Practical Text Analytics: Maximizing the Value of Text Data. Cham: Springer Nature.
Blei, D.M., Ng, A.Y. & Jordan, M.I., 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research, [online] Tersedia di: <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>
Chatterjee, M., Kumar, P., Samanta, P. & Sarkar, D., 2022. Suicide Ideation Detection from Online Social Media: A Multi-Modal Feature Based Technique. International Journal of Information Management Data Insights, 2(2), p.100103.
George, C.P. & Doss, H., 2018. Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model. Journal of Machine Learning Research, [online] Tersedia di: <https://www.jmlr.org/papers/v18/15-595.html>
Hamdan, S., Berkman, N., Lavi, N., Levy, S. & Brent, D., 2019. The Effect of Sudden Death Bereavement on the Risk for Suicide. Crisis.
Kamarudin, N.S., Beigi, G. & Liu, H., 2021. A Study on Mental Health Discussion through Reddit. In: 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM). Pekan, Malaysia, 24-26 August 2021. IEEE.
Kim, W.J., Song, Y.J., Ryu, H.S., Ryu, V., Kim, J.M., Ha, R.Y., Lee, S.J., Namkoong, K., Ha, K. & Cho, H.S., 2015. Internalized Stigma and Its Psychosocial Correlates in Korean Patients with Serious Mental Illness. Psychiatry research, 225(3), pp.433-439.
Mardones-Segovia, C., Wheeler, J.M., Choi, H.J., Wang, S. & Cohen, A.S., 2023. Model Selection for Latent Dirichlet Allocation in Assessment Data. Psychological Test and Assessment Modeling, 65(1), pp.3-35.
Putri, I.R. & Kusumaningrum, R., 2017. Latent Dirichlet allocation (LDA) for Sentiment Analysis toward Tourism Review in Indonesia. Journal of Physics: Conference Series, [online] Tersedia di: <https://iopscience.iop.org/article/10.1088/1742-6596/801/1/012073/meta>
Rabani, S.T., Khan, Q.R. & Khanday, A.M.U.D., 2020. Detection of Suicidal Ideation on Twitter using Machine Learning & Ensemble Approaches. Baghdad science journal, 17(4), pp.1328-1328.
Röder, M., Both, A. & Hinneburg, A., 2015. Exploring the Space of Topic Coherence Measures. In: Proceedings of the eighth ACM international conference on Web search and data mining. Shanghai, China, 2-6 February 2015. New York: ACM.
Sievert, C. & Shirley, K., 2014. LDAvis: A Method for Visualizing and Interpreting Topics. In: Proceedings of the workshop on interactive language learning, visualization, and interfaces. Maryland, USA, 27 June 2014. ACL.
Sik, D., Németh, R. & Katona, E., 2021. Topic Modelling Online Depression Forums: Beyond Narratives of Self-Objectification and Self-Blaming. Journal of Mental Health, 32(2), pp.386-395.
Song, H., You, J., Chung, J.W. & Park, J.C., 2018. Feature Attention Network: Interpretable Depression Detection from social Media. Proceedings of the 32nd Pacific Asia conference on language, information and computation. Hong Kong, 1-3 December 2018. ACL.
Syed, S. & Spruit, M., 2017. Full-Text or Abstract? Examining Topic Coherence Scores using Latent Dirichlet Allocation. In: 2017 IEEE International conference on data science and advanced analytics (DSAA). Tokyo, Japan, 19-21 October 2017. IEEE.
Tadesse, M.M., Lin, H., Xu, B. & Yang, L., 2019. Detection of Depression-Related Posts in Reddit Social Media Forum. IEEE Access, 7, pp.44883-44893.
Turcan, E., Muresan, S. & McKeown, K., 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. 6–11 June 2021. ACL.
Wagner, B., Hofmann, L. & Grafiadeli, R., 2021. The Relationship Between Guilt, Depression, Prolonged grief, and Posttraumatic Stress Symptoms After Suicide Bereavement. Journal of Clinical Psychology, 77(11), pp.2545-2558.
WHO, 2023. Suicide. [online] Tersedia di: <https://www.who.int/news-room/fact-sheets/detail/suicide>
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