CONSTRUCTING SCIENTIFIC CLAIMS IN ONLINE HERBAL DISCOURSE: A COMPUTATIONAL LINGUISTIC ANALYSIS OF KASKUS THREADS
DOI:
https://doi.org/10.21009/ishel.v2i1.70927Keywords:
herbal, kaskus, assertive, scientific tone, claim, computationalAbstract
This study examines how claims are constructed and how scientific tone is linguistically realized in online herbal discourse. Its novelty lies in integrating computational text analysis with discourse analysis to evaluate the scientific quality of online herbal health claims in the Indonesian context. Using a quantitative design, data were collected from fifteen herbal-treatment discussion threads on the Kaskus forum. After Python-based preprocessing, including tokenization, data cleaning, and text normalization, the final corpus comprised 572 files containing 77 claim sentences. Claim construction was analyzed through assertiveness markers and claim types based on Biber’s (2006) stance framework. Scientific tone was measured as a continuous numerical score using linguistic features adapted from McNamara et al. (2010). Toulmin’s argumentation model, as elaborated by Hample (1977), was applied to interpret the relationship between linguistic form and argumentative justification. The findings show that rhetorical strength does not necessarily reflect scientific tone. Linguistically assertive claims often exhibit low scientific tone because they rely on evaluative and popular language, whereas less assertive claims may achieve a moderate scientific tone through evidential markers and more formal linguistic features. These findings contribute to digital discourse and health communication research by distinguishing claim strength from scientific tone and provide practical insights for improving health literacy and critical evaluation of online health claims.
References
Biber, D. (2006). Stance in discourse: Subjectivity, evaluation, and interaction. In S. Hunston & G. Thompson (Eds.), Evaluation in text: Authorial stance and the construction of discourse (pp. 97–122). Oxford University Press.
Hample, D. (1977). The Toulmin model of argumentation. In J. L. Golden, G. F. Berquist, & W. E. Coleman (Eds.), The rhetoric of Western thought (pp. 444–460). Kendall/Hunt.
Kristiyanti, M. (2021). Online discourse of traditional medicine in Indonesian digital communities. Journal of Indonesian Health Communication, 5(2), 112–128.
Li, Y., Zhang, J., & Yu, B. (2017, September). An NLP analysis of exaggerated claims in science news. In Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism (pp. 106-111).
Lim, M. (2013). Many clicks but little sticks: Social media activism in Indonesia. Journal of Contemporary Asia, 43(4), 636–657. https://doi.org/10.1080/00472336.2013.769386
McNamara, D. S., Crossley, S. A., & McCarthy, P. M. (2010). Linguistic features of writing quality. Written Communication, 27(1), 57–86. https://doi.org/10.1177/0741088309351547
McNamara, D. S., Graesser, A. C., McCarthy, P. M., & Cai, Z. (2014). Automated evaluation of text and discourse with Coh-Metrix. Cambridge University Press.
Miner, A. S., Milstein, A., Schueller, S., Hegde, R., Mangurian, C., & Linos, E. (2022). Smartphone-based conversational agents and responses to mental health crises. JAMA Internal Medicine, 182(1), 1–8. https://doi.org/10.1001/jamainternmed.2021.6454
Nugroho, Y. (2020). Digital participation and public discourse in Indonesia. Asian Journal of Communication, 30(3–4), 219–235. https://doi.org/10.1080/01292986.2020.1785164
Sardinha, T. B. (2022). Corpus linguistics and the study of social media: A case study using multi-dimensional analysis. In The Routledge handbook of corpus linguistics (pp. 656-674). Routledge.
Sharma, U., Saran, S., & Patil, S. M. (2020). Fake news detection using machine learning algorithms. International Journal of creative research thoughts (IJCRT), 8(6), 509-518.
Sujarwo, W., Arinasa, I. B. K., Salomone, F., Caneva, G., & Fattorini, S. (2021). Cultural erosion of traditional medicinal knowledge in Indonesia. Journal of Ethnopharmacology, 274, 114–132. https://doi.org/10.1016/j.jep.2021.114132
Utami, N. S. (2022). Narratives of herbal medicine in Indonesian online forums. Humaniora, 34(1), 45–58.
WIbowo, A. P., & Hidayat, N. (2023). Eksplorasi Linguistik Komputasional dalam Analisis Bahasa Alami untuk Mengungkap Evolusi Dialek Digital di Era Media Sosial Global. Journal of New Trends in Sciences, 1(3), 45-52.
Windari, T., Rahmawati, R., & Prasetyo, A. (2022). Public perception of herbal medicine consumption in Indonesia. Journal of Public Health Research, 11(2), 227–235. https://doi.org/10.4081/jphr.2022.227
Wirawan, A., & Simarmata, J. (2023). Health misinformation on social media during public health crises in Indonesia. Journal of Communication Inquiry, 47(1), 77–95. https://doi.org/10.1177/01968599221125698
Zhang, Y., Lyu, H., Liu, Y., Zhang, X., Wang, Y., & Luo, J. (2020). Monitoring misinformation related to COVID-19 in online communities. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW1), 1–20. https://doi.org/10.1145/3392831
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