Semantic Equivalence of Algospeak as a Register in Social Media based on Semantic Prosody Theory
Abstract
The phenomenon of algospeak usage in social media reflects the emergence of linguistic masking strategies that enable negatively toned messages to be conveyed implicitly while remaining concealed from algorithmic monitoring systems. A review of the literature indicates that empirical approaches remain limited in examining the visual forms of algospeak disguises that are increasingly widespread in digital discourse, as well as their impact on the original meanings of the words involved. Therefore, this study was conducted to identify the categories of word types commonly disguised to represent negative human traits and to examine the semantic equivalence between algospeak disguised forms and their original lexical forms through the lens semantic prosody theory. A total of 5,000 public comments from Facebook were extracted using the PhantomBuster application as the primary data source. Data collection was carried out through an Application Programming Interface (API) supported by an automated script specifically developed to systematically extract user comments. The analysis was conducted qualitatively through open coding and thematic analysis, followed by the application of semantic prosody theory with an emphasis on collocational patterns and evaluative meanings underlying the utterances. The findings reveal that social media users tend to target individual weaknesses in physical, moral, intellectual, social, and ideological domains through algospeak forms, such as “l3mb00”, “p3ny0nd0l”, and “m4c41 p0l1t!k”. Nevertheless, the results demonstrate that orthographic transformations do not weaken the semantic force of the original words, as the accompanying collocational and evaluative contexts consistently preserve their implicit meanings. Overall, the application of semantic prosody theory proves effective in addressing ambiguity in disguised language and contributes to the empirical and contextualised advancement of digital semantic discourse, particularly by providing a methodological foundation for analysing lexical masking strategies on social media, as well as offering practical guidance for digital linguistics research, language education, and digital content management.
Keywords: Algospeak, implicit meaning, semantic prosody, social media, collocation, linguistic masking
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References
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2. Ali Muttaleb Hasan, Noorhuzaimi Mohd Noor, Taha Hussein Rassem, Shahrul Azman Mohd Noah & Ahmed Muttaleb Hasan. (2020). A proposed method using the semantic similarity of wordnet 3.1 to handle the ambiguity to apply in social media text. Dlm. K. J. Kim, & H. Y. Kim. (Pnyt.), Information science and applications (hlm. 621). Springer, Singapore. https://doi.org/10.1007/978-981-15-1465-4_47
3. Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. SAGE Publications.
4. Cai, J., Ishimizu, Y., Zhang, M., Li, M., Li, J., & Tei, K. (2025). Simulation of language evolution under regulated social media platforms: A synergistic approach of large language models and genetic algorithms. IEEE Transactions on Computational Social Systems, 8.X(X), 1–12. https://arxiv.org/pdf/2502.19193
5. Camaj, L., Hellmueller, L., Vallejo Vera, S., & Lindner, P. (2024). The democratic value of strategic game reporting and uncivil talk: A computational analysis of Facebook conversations during US Primary Debates. Journalism and Mass Communication Quarterly, 101(2), 428–450. https://doi.org/10.1177/10776990231226403
6. Davis, W. (2023). Implicature: Intention, convention, and principle in the failure of Gricean theory. Oxford University Press.
7. Dean Yeo, & Su-Hie Ting. (2017). Netspeak features in Facebook communication of Malaysian university students. Journal of Advanced Research in Social and Behavioural Sciences, 6(1), 81–90.
8. Devi, M. D., & Saharia, N. (2024). Identification of domain-specific euphemistic tweets using clustering. International Journal of Information Technology, 16(1), 21–31.
9. Di Marco, N., Loru, E., Bonetti, A., Serra, A. O. G., Cinelli, M., & Quattrociocchi, W. (2024). The evolution of language in social media comments. Physics and Society. https://arxiv.org/pdf/2406.11450
10. Eiswirth, M. E. (2020). Evaluating the representativeness of a small qualitative sample with data from the Diabetes Online Community on Twitter: A mixed methods study (Preprint). JMIR Publications. https://preprints.jmir.org/preprint/25253
11. Ezeudo, C. O. (2024). Exploring the impact of social media on language use and literacy skills. Crowther Journal of Arts and Humanities, 1(6), 86–99.
12. Farkas, J., & Neumayer, C. (2018). Disguised propaganda from digital to social media. Dlm. J. Hunsinger, L. Klastrup, & M. Allen, (Pnyt.), Second international handbook of internet research (hlm. 1–17). Springer.
13. Farrow, K., Grolleau, G., & Mzoughi, N. (2021). ‘Let’s call a spade a spade, not a gardening tool’: How euphemisms shape moral judgement in corporate social responsibility domains. Journal of Business Research, 131, 254–267.
14. Fazal Mohamed Mohamed Sultan, & Muhammad Syafreza Azwan Pisol. (2021). Slanga dalam media sosial: Satu pendekatan minimalis. E-Bangi, Journal of Social Science and Humanities, 19(3), 151–161.
15. Fillies, J., & Paschke, A. (2024). Simple LLM based approach to counter laras algoritma. Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024) (hlm. 136–145). https://aclanthology.org/2024.woah-1.10/
16. FullEnrich. (t. t.). Phantombuster: In-depth analysis. https://fullenrich.com/tools/Phantombuster-vs-Datagma
17. Fuqua, J. (2019). Semantic prosody: The phenomenon of ‘prosody’ in lexical patterning. Journal of Language Teaching and Learning, 4(2), 76–83.
18. Gerbaudo, P. (2024). TikTok and the algorithmic transformation of social media publics: From social networks to social interest clusters. New Media and Society, 0(0). https://doi.org/10.1177/14614448241304106
19. Gohil, H., & Smith, V. (2024). An analysis of algospeak and the dreaded algorithm. International Journal of Advanced Research, 12(10), 656–669. https://dx.doi.org/10.21474/IJAR01/19684
20. Grabowski, L., & Trklja, A. (2024). Unravelling the complexity of semantic prosody: A theoretical inquiry. Journal of Pragmatics, 226, 89–102.
21. Grieve, J., Nini, A., & Guo, D. (2018). Mapping lexical innovation on American social media. Journal of English Linguistics, 46(4), 293–319.
22. Hishamudin Isam. (2024). Laras algoritma and digital culture: Navigating social media challenges. Proceedings of the Third International Conference on Communnication, Language, Litearture and Culture (ICCOLlic 2024), 10 September 2024, UNS Tower Hotel, Surakarta, Indonesia. https://www.atlantis-press.com/proceedings/iccollic-24/126006853
23. Hishamudin Isam. (2025). From digital technology to society: Laras algoritma and communication styles on social media in the Era of Artificial Intelligence (AI). Proceedings of the International Joint Conference on UNESA, 2(2), 88–104. https://proceeding.unesa.ac.id/index.php/pijcu/article/view/4461
24. Jahan, R., & Irfan, M. (2021). Semantic change in English language: Social media neologisms. Pakistan Languages and Humanities Review, 5(2), 638–646. http://doi.org/10.47205/plhr.2021(5-II)2.47
25. Klug, D., Steen, F. F., & Yurechko, M. (2023). How algorithm awareness impacts laras algoritma use on TikTok. Companion Proceedings of the ACM Web Conference 2023, Austin, TX, USA. https://dl.acm.org/doi/pdf/10.1145/3543873.3587355?casa_token
26. Mandy Lau. (2022). Content moderation as language policy: Connecting commercial content moderation policies, regulations, and language policy. Working Papers in Applied Linguistics and Linguistics at York, 2, 1–12. https://doi.org/10.25071/2564-2855.11
27. Mardina Hj Mahadi, & Nurul Azyan Zunaidi. (2021). Analisis kata pikat dalam media sosial: Satu kajian semantik. Jurnal Bahasa, 21(1), 129–162. https://doi.org/10.37052/jb21(1)no7
28. Mohd Norazizi Samsudain, & Mardian Shah Omar. (2022). Penggunaan unsur bahasa negatif terhadap hantaran isu 1MDB oleh pengguna Facebook. Jurnal Pengajian Melayu, 33(1), 87–107.
29. Moskal, M., & Supernak, N. (2023). Do you speak laras algoritma? An introduction to the recent yet prominent phenomenon of Internet discourse from a cognitive linguistics perspective. Proceeding of Poznańskie Forum Kognitywistyczne. http://pfk.home.amu.edu.pl/
30. M. S. Nik Puteri Fatin Nor’ain, & Maslida Yusof. (2024). Bahasa singkatan dalam komunikasi di media sosial dalam kalangan Generasi Z. Jurnal Linguistik, 28(2), 1–18.
31. Narayanan, A. (2023, 9 Mac). Understanding social media recommendation algorithms. Knight First Amendment Institute. https://knightcolumbia.org/content/understanding-social-media-recommendation-algorithms
32. Nurasia Natsir, Aziza Aliah, Zulkhaeriyah Zulkhaeriyah, Amirudin Amirudin, & Farida Esmianti. (2023). The impact of language changes caused by technology and social media. Language Literacy: Journal of Linguistics Literature and Language Teaching, 7(1), 115–124.
33. Nur Nashatul Nasuha Nazman, Su-Hie Ting, & Kee-Man Chuah. (2023). Lexical innovation processes of youth netspeak on Malay Twitter posts. GEMA Online® Journal of Language Studies, 23(1), 40–59. http://doi.org/10.17576/gema-2023-2301-03
34. Page, R., Barton, D., Unger, J. W., & Zappavigna, M. (2014). Researching language and social media: A student guide. Routledge.
35. PhantomBuster. (2024). Documentation and features overview. https://phantombuster.com
36. Pocock, E. (2025). Investigating “laras algoritma” on social media platforms: A semi systematic meta narrative literature review with Orange based text mining [Tesis sarjana, University of Boras, Sweden]. https://hb.diva-portal.org/smash/record.jsf?pid=diva2%3A1959435
37. Pornthip Supanfai. (2017). Semantic prosody in Thai [Tesis doktor falsafah, Lancaster University]. https://www.research.lancs.ac.uk/portal/en/publications/semantic-prosody-in-thai(7e27e306-c147-4cea-9786-f142cc6bf012).html
38. Pusat Rujukan Persuratan Melayu. (2025). https://prpm.dbp.gov.my/Cari1?keyword=eufemisme&d=250808&
39. Reagle, J. M., & Gaur, M. (2022). Spinning words as disguise: Shady services for ethical research? First Monday, 27(1), 1–21. https://doi.org/10.5210/fm.v27i1.12350
40. Rossini, P. G. C., Stromer-Galley, J., & Zhang, F. (2020). Exploring the relationship between campaign discourse on Facebook and the public’s comments: A case study of incivility during the 2016 US presidential election. Political Studies, 69(1), 89-107. https://doi.org/10.1177/0032321719890818
41. Russnes, M. (2024). Semantic prosody, semantic transfer and semantic change. ICAME Journal, 48(1), 67–88. https://doi.org/10.2478/icame-2024-0004
42. Sarhad, J. S., & Mahmod, R. A. (2023). A corpus-based study of semantic prosody across a native corpus. Journal of the University of Garmian, 10(3), 902–909. https://doi.org/10.24271/garmian.2023.10378
43. Serodio, P. M., Al Baghal, T., Sloan, L., Liu, S., & Jessop, C. (2024). Augmenting surveys with social media data: A probabilistic framework for Linkedin data linkage. International Journal of Population Data Science, 9(4). https://doi.org/10.23889/ijpds.v9i4.2433
44. Sigit Haryanto, Rani Setiawaty, Laily Rahmatika, Agus Budi Wahyudi, Mohammed Shamsul Hoque, Fatikhatun Najikhah, & M. Monjurul Islam. (2023). The use of social media: Metaphorical euphemism in Indonesian President’s Facebook comments. LiNGUA: Jurnal Ilmu Bahasa dan Sastra, 18(1), 107–118.
45. Singer, N. (2022). The negative effects of social media algorithms [Tesis sarjana muda, Schreyer Honors College, The Pennsylvania State University]. United State of America. https://honors.libraries.psu.edu/files/final_submissions/8388
46. Steen, E., Yurechko, K., & Klug, D. (2023). You can (not) say what you want: Using laras algoritma to contest and evade algorithmic content moderation on TikTok. Social Media + Society, 9(3), 1–17. https://doi.org/10.1177/20563051231194586
47. Stewart, D. (2010). Semantic prosody: A critical evaluation. Routledge.
48. Wulansari. (2021). The collocation meaning of “vaccine” on semantic approach: A corpus-based analysis. Haluan Sastra Budaya, 5(1), 77–88.
49. Yang, C. (2024). Explorations in semantic prosodies study based on near synonyms. Evaluation of Educational Research, 2(1). https://doi.org/10.18686/eer.v2i1.3483.
50. Yi Shan. (2023). Form (prosody)-meaning (pragmatics) pairings of discourse markers: A case study of nǐ zhīdào (‘you know’) as a construction in Chinese media interviews. Language & Communication, 93, 136–154. https://doi.org/10.1016/j.langcom.2023.09.004
51. Zhang, C. (2010). An overview of corpus-based studies of semantic prosody. Asian Social Science, 6(6), 190–194.
52. Zhao, X. (2025). Social media data analysis and application research in the era of digital economy. Journal of Business and Economic Research, 1(1), 59–64. https://www.sci-open.net/index.php/JBER/article/view/415/433
53. Zulfati Izazi Zulkifli, & Tengku-Sepora Tengku Mahadi. (2020). Slangs on social media: ariations among Malay language users on twitter. Pertanika Journal of Social Sciences and Humanities, 28(1), 17–34.
Published
2025-11-28
How to Cite
ISAM, Hishamudin.
Semantic Equivalence of Algospeak as a Register in Social Media based on Semantic Prosody Theory.
Jurnal Bahasa, [S.l.], v. 25, n. 2, p. 235-264, nov. 2025.
ISSN 2462-1889.
Available at: <http://jurnal.dbp.my/index.php/jurnalbahasa/article/view/9151>. Date accessed: 08 jan. 2026.
doi: https://doi.org/10.37052/jb25(2)no2.
Section
Articles
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