Abstract
This study tackles the pressing issue of predicting suicidal tendencies on Twitter by introducing an inventive methodology that integrates Generative Adversarial Networks (GAN), Deep Learning methodologies, Word Embedding Fusion, and Genetic Algorithms. The framework is devised to capitalize on the unique strengths of each element, resulting in a more resilient and precise predictive model. The process initiates with the aggregation of Twitter posts, which undergo preprocessing and transformation into word embeddings. These embeddings establish the groundwork for subsequent stages. The utilization of GAN comes into play for generating synthetic data, enriching the dataset’s diversity and volume—an essential aspect for the effective training of deep learning models. Concurrently, a deep learning architecture is employed to decipher intricate patterns within the data. The harmonization of GAN-generated samples and genuine data optimizes the model's performance, ensuring a comprehensive comprehension of features indicative of suicidal ideation. The integration of Word Embedding Fusion further enhances the model by amalgamating information from diverse embedding techniques, leveraging their distinct perspectives. This step boosts the model’s capacity to interpret and generalize patterns, thereby contributing to an augmented prediction accuracy. The inclusion of Genetic Algorithms introduces an evolutionary optimization layer to the process. Genetic Algorithms play a crucial role in fine-tuning model parameters, optimizing overall performance by iteratively evolving candidate solutions. This not only enhances the model's predictive capabilities but also facilitates adaptability to the varied and dynamic linguistic expressions found on Twitter. Our work distinguishes itself from existing methodologies through this comprehensive approach that encompasses GAN, Deep Learning, Word Embedding Fusion, and Genetic Algorithm. The amalgamation of these techniques amplifies the predictive accuracy and resilience of the model, exhibiting promising results in the challenging task of predicting suicidal tendencies on Twitter. The adaptability of the proposed framework positions it as a potent tool for real-world applications, aiding in the timely identification and intervention in potential cases of self-harm.
Similar content being viewed by others
Data availability
The true novelty of this study resides in emphasizing the utilization of current public opinion data, as opposed to working with outdated information sourced from repositories. This approach enables authorities to make informed decisions by staying attuned to public sentiments, thereby enhancing their ability to serve the public more effectively. The dataset supporting the findings of this study are available upon request. Access to the dataset is subject to approval and may be granted based on the nature of the request. Researchers interested in obtaining access to the data should contact to initiate the request process.
References
Martinez-Ales G, Hernandez-Calle D, Khauli N, Keyes KM (2020) Why are suicide rates increasing in the United States? Towards a multilevel reimagination of suicide prevention. In: Baca-Garcia E (ed) Behavioral neurobiology of suicide and self harm, vol 46. Springer International Publishing, Cham, pp 1–23. https://doi.org/10.1007/7854_2020_158
Fleischmann A et al (2016) Overview evidence on interventions for population suicide with an eye to identifying best-supported strategies for LMICs. Glob Ment Health 3:e5. https://doi.org/10.1017/gmh.2015.27
Vijayakumar L (2017) Challenges and opportunities in suicide prevention in South-East Asia. WHO South-East Asia J Public Health 6(1):30. https://doi.org/10.4103/2224-3151.206161
Bachmann S (2018) Epidemiology of suicide and the psychiatric perspective. IJERPH 15(7):1425. https://doi.org/10.3390/ijerph15071425
Sinyor M, Tse R, Pirkis J (2017) Global trends in suicide epidemiology. Curr Opin Psychiatry 30(1):1–6. https://doi.org/10.1097/YCO.0000000000000296
Glenn CR et al (2020) Annual research review: a meta-analytic review of worldwide suicide rates in adolescents. Child Psychol Psychiatry 61(3):294–308. https://doi.org/10.1111/jcpp.13106
Ivbijaro G et al (2019) Preventing suicide, promoting resilience: Is this achievable from a global perspective? Asia Pac Psychiatry 11(4):e12371. https://doi.org/10.1111/appy.12371
Arensman E, Scott V, De Leo D, Pirkis J (2020) Suicide and suicide prevention from a global perspective. Crisis 41(Supplement 1):S3–S7. https://doi.org/10.1027/0227-5910/a000664
Pandey S, Sharma S, Wazir S (2022) Mental healthcare chatbot based on natural language processing and deep learning approaches: ted the therapist. Int J Inf Tecnol 14(7):3757–3766. https://doi.org/10.1007/s41870-022-00999-6
Saxena M, Bagga T, Gupta S (2021) Fearless path for human resource personnel’s through analytics: a study of recent tools and techniques of human resource analytics and its implication. Int J Inf Tecnol 13(4):1649–1657. https://doi.org/10.1007/s41870-021-00677-z
Aladağ AE, Muderrisoglu S, Akbas NB, Zahmacioglu O, Bingol HO (2018) Detecting suicidal ideation on forums: proof-of-concept study. J Med Internet Res 20(6):e215. https://doi.org/10.2196/jmir.9840
Sekulic I, Strube M (2019) Adapting deep learning methods for mental health prediction on social media. In Proceedings of the 5th workshop on noisy user-generated text (W-NUT 2019), Hong Kong, China: association for computational linguistics, pp. 322–327. doi: https://doi.org/10.18653/v1/D19-5542.
Chancellor S, De Choudhury M (2020) Methods in predictive techniques for mental health status on social media: a critical review. npj Digit Med 3(1):43. https://doi.org/10.1038/s41746-020-0233-7
Hao B, Li L, Li A, Zhu T (2013) Predicting Mental health status on social media: a preliminary study on microblog. In: Rau PLP (ed) Cross-cultural design. Cultural differences in everyday life, vol 8024. Springer, Berlin, Heidelberg, pp 101–110. https://doi.org/10.1007/978-3-642-39137-8_12
Ophir Y, Tikochinski R, Asterhan CSC, Sisso I, Reichart R (2020) Deep neural networks detect suicide risk from textual facebook posts. Sci Rep 10(1):16685. https://doi.org/10.1038/s41598-020-73917-0
Sawhney R, Manchanda P, Mathur P, Shah R, Singh R (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, Brussels, Belgium: association for computational linguistics, 2018, pp. 167–175. https://doi.org/10.18653/v1/W18-6223.
Ji S, Yu CP, Fung S, Pan S, Long G (2018) Supervised learning for suicidal ideation detection in online user content. Complexity 2018:1–10. https://doi.org/10.1155/2018/6157249
C. Lin et al (2020) SenseMood Depression detection on social media. In: Proceedings of the 2020 international conference on multimedia retrieval. ACM, 2020. https://doi.org/10.1145/3372278.3391932.
Weng J-C, Lin T-Y, Tsai Y-H, Cheok M, Chang Y-P, Chen V (2020) An autoencoder and machine learning model to predict suicidal ideation with brain structural imaging. JCM 9(3):658. https://doi.org/10.3390/jcm9030658
FHK dos S. Tanaka, and C. Aranha (2019) Data augmentation using GANs. arXiv. Accessed 14 Nov 2023. [Online]. Available: http://arxiv.org/abs/1904.09135
Shao S, Wang P, Yan R (2019) Generative adversarial networks for data augmentation in machine fault diagnosis. Comput Ind 106:85–93. https://doi.org/10.1016/j.compind.2019.01.001
A Biswas et al. (2023) Generative adversarial networks for data augmentation. 2023. Doi: https://doi.org/10.48550/ARXIV.2306.02019
Konidaris F, Tagaris T, Sdraka M, Stafylopatis A (2019) Generative adversarial networks as an advanced data augmentation technique for mri data. In: Proceedings of the 14th international joint conference on computer vision, imaging and computer graphics theory and applications, Prague, Czech Republic: SCITEPRESS - Science and Technology Publications, pp. 48–59. doi: https://doi.org/10.5220/0007363900480059.
Dieudonat L, Han K, Leavitt P, Marquer E (2020) Exploring the combination of contextual word embeddings and knowledge graph embeddings. arXiv, Accessed: Nov. 14, 2023. [Online]. Available: http://arxiv.org/abs/2004.08371
Akbik A, Duncan A, Blythe J, Roland V (2018) Contextual string embeddings for sequence labelling. In: International conference on computational linguistics. [Online]. Available: https://api.semanticscholar.org/CorpusID:52010710
Li Y, Yang T (2018) Word embedding for understanding natural language: a survey. In: Srinivasan S (ed) Guide to big data applications, vol 26. Springer International Publishing, Cham, pp 83–104. https://doi.org/10.1007/978-3-319-53817-4_4
Miaschi A, Dell’Orletta F (2020) Contextual and non-contextual word embeddings: an in-depth linguistic investigation. In: Proceedings of the 5th workshop on representation learning for NLP, Online: association for computational linguistics. pp. 110–119. https://doi.org/10.18653/v1/2020.repl4nlp-1.15
Pierre L, Andrei K (2017) Redefining context windows for word embedding models: an experimental study. In: Nordic conference of computational linguistics. [Online]. Available: https://api.semanticscholar.org/CorpusID:7736753
Immanuel SD,Chakraborty UKr (2019) Genetic algorithm: an approach on optimization. In: 2019 international conference on communication and electronics systems (ICCES), Coimbatore, India: IEEE, pp. 701–708. https://doi.org/10.1109/ICCES45898.2019.9002372.
Hamdia KM, Zhuang X, Rabczuk T (2021) An efficient optimization approach for designing machine learning models based on genetic algorithm. Neural Comput Appl 33(6):1923–1933. https://doi.org/10.1007/s00521-020-05035-x
García JM, Acosta CA, Mesa MJ (2020) Genetic algorithms for mathematical optimization. J Phys Conf Ser 1448(1):012020. https://doi.org/10.1088/1742-6596/1448/1/012020
Zang W, Ren L, Zhang W, Liu X (2018) A cloud model based DNA genetic algorithm for numerical optimization problems. Futur Gener Comput Syst 81:465–477. https://doi.org/10.1016/j.future.2017.07.036
Ji S, Pan S, Li X, Cambria E, Long G, Huang Z (2021) Suicidal ideation detection: a review of machine learning methods and applications. IEEE Trans Comput Soc Syst 8(1):214–226. https://doi.org/10.1109/TCSS.2020.3021467
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflicts of interest. All co-authors have reviewed and approved the contents of the manuscript, and there are no financial interests to report. We affirm that the submission represents original work and is not currently under review by any other publication.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kancharapu, R., Ayyagari, S.N. Suicidal ideation prediction based on social media posts using a GAN-infused deep learning framework with genetic optimization and word embedding fusion. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-023-01725-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s41870-023-01725-6