Dialects, Emojis, and Engagement: A Corpus-Based Study of Arabic Linguistic Features in TikTok Comments

Menik Mahmudah; Mohammad Ahsanuddin; Muhamad Solehudin; Nur Hanifansyah; Aufa Hanin Salsabil; Aida Zavirah Fayruza

doi:10.30762/asalibuna.v10i01.8390

Authors

Menik Mahmudah Universitas Negeri Malang
Mohammad Ahsanuddin Universitas Negeri Malang
Muhamad Solehudin Universitas Islam Internasional Darullughah Wadda’wah
Nur Hanifansyah Universitas Islam Internasional Darullughah Wadda'wah
Aufa Hanin Salsabil International Islamic University Malaysia
Aida Zavirah Fayruza Universitas Negeri Malang

DOI:

https://doi.org/10.30762/asalibuna.v10i01.8390

Keywords:

Arabic corpus linguistics, TikTok comments, digital discourse, dialectal Arabic, code-switching, Multimodality, linguistik korpus bahasa Arab, Komentar TikTok, Wacana Digital, Bahasa Arab Dialek, Alih Kode, Multimodal

Abstract

This study employed a mixed-methods corpus-based discourse analysis to examine the linguistic features of Arabic used in TikTok comments. The data consisted of 2,160 Arabic comments collected from 36 TikTok videos published between September 2025 and January 2026. After data cleaning, the final corpus contained 28,742 tokens and 6,914-word types. Quantitative corpus procedures were conducted using AntConc 4.3.1 and Sketch Engine to generate frequency lists, normalized frequencies, concordance lines, and lexical clusters. The quantitative results were subsequently integrated with qualitative discourse-pragmatic interpretation informed by Computer-Mediated Communication, pragmatics, and discourse analysis. The findings show that Arabic in TikTok comments is predominantly informal, interactive, and multimodal. The dominant features include dialectal expressions, religious language, orthographic elongation, repetition, emoji usage, Arabic–English code-switching, and engagement-driven language. Emoji and multimodal elements were the most frequent feature (38.7%), followed by dialectal expressions (28.3%) and engagement-driven language (19.5%). These results indicate that communication on TikTok resembles spoken language that is expressive and socially oriented rather than formal written Arabic. Theoretically, this study extends Arabic corpus linguistics by focusing on an underexplored digital platform. Practically, the findings provide useful implications for Arabic digital language pedagogy and the development of NLP systems that can better handle non-standard Arabic varieties. The study also calls for expanding Arabic NLP frameworks to accommodate multimodal and non-standard digital registers.

Abstrak:

Penelitian ini menggunakan analisis wacana berbasis korpus dengan desain metode campuran untuk mengkaji karakteristik linguistik bahasa Arab dalam komentar TikTok. Data penelitian terdiri atas 2.160 komentar berbahasa Arab yang dikumpulkan dari 36 video TikTok yang dipublikasikan antara September 2025 dan Januari 2026. Setelah proses pembersihan data, korpus akhir berisi 28.742 token dan 6.914 tipe kata. Prosedur kuantitatif berbasis korpus dilakukan menggunakan AntConc 4.3.1 dan Sketch Engine untuk menghasilkan daftar frekuensi, frekuensi ternormalisasi, baris konkordansi, dan klaster leksikal. Hasil kuantitatif tersebut kemudian diintegrasikan dengan interpretasi kualitatif wacana-pragmatik berdasarkan teori Computer-Mediated Communication, pragmatik, dan analisis wacana. Hasil penelitian menunjukkan bahwa bahasa Arab dalam komentar TikTok bersifat informal, interaktif, dan multimodal. Fitur dominan meliputi ekspresi dialektal, ungkapan religius, pemanjangan ortografis, repetisi, penggunaan emoji, alih kode Arab–Inggris, serta bahasa berbasis keterlibatan (engagement). Emoji dan elemen multimodal muncul paling tinggi (38,7%), diikuti ekspresi dialektal (28,3%) dan bahasa berbasis keterlibatan (19,5%). Temuan ini menunjukkan bahwa komunikasi di TikTok lebih menyerupai bahasa lisan yang ekspresif dan berorientasi relasi sosial daripada bahasa tulis formal. Secara teoretis, penelitian ini memperluas kajian linguistik korpus bahasa Arab pada platform digital yang masih kurang dieksplorasi. Secara praktis, hasil penelitian ini bermanfaat bagi pengembangan pedagogi bahasa Arab digital dan sistem NLP bahasa Arab yang adaptif terhadap ragam non-standar. Penelitian ini juga menekankan perlunya pengembangan dan perluasan kerangka kerja NLP bahasa Arab agar mampu mengakomodasi ragam bahasa digital yang bersifat multimodal serta non-standar.

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References

Alanazi, M. S. (2024). The use of Modern Standard Arabic and colloquial Arabic in translation tasks: A new perspective. Cogent Arts & Humanities, 11(1), 2366572. https://doi.org/10.1080/23311983.2024.2366572

Alkadi, H., Almoaily, M., & Alsuhaibani, Y. (2026). From Formality to Familiarity: A Content Analysis of Linguistic Shifts in Written Arabic on YouTube. International Journal of Applied Linguistics, 36(1), 904–915. https://doi.org/10.1111/ijal.12825

Al-khawaldeh, N., Mashaqba, B., Al-Khawaldeh, S., Al Huneety, A., & AlQbailat, N. (2024). Irony in Jordanian Arabic on Social Media: A Corpus-Based Analysis of Linguistic and Rhetorical Devices. International Journal of Society, Culture and Language, 12(2). https://doi.org/10.22034/ijscl.2024.2021255.3363

AlMotairi, R. K., & Hadwan, M. (2024). Sentiment Analysis Methods for Arabic Content on Social Media: A Systematic Review. Ingénierie Des Systèmes d Information, 29(1), 389–396. https://doi.org/10.18280/isi.290138

Al-Shenaifi, N., Azmi, A. M., & Hosny, M. (2024). Advancing AI-Driven Linguistic Analysis: Developing and Annotating Comprehensive Arabic Dialect Corpora for Gulf Countries and Saudi Arabia. Mathematics, 12(19), 3120. https://doi.org/10.3390/math12193120

Arisandi, Y., & Habib, Moh. T. (2025). Optimizing YouTube for Interactive Arabic Learning in Pesantren: Effective Content Creation Strategies. International Journal of Arabic Language Teaching, 7(02), 239–254. https://doi.org/10.32332/ijalt.v7i02.10363

Baharun, S., Hanifansyah, N., Habib, Moh. T., Arifin, A., Syaifullah, M., & Faqihah, N. A. (2026). YouTube Content Production for Arabic Language Learning: Cross-National Study. Online Learning In Educational Research (OLER), 6(1), 13–27. https://doi.org/10.58524/oler.v6i1.982

Brown, P., & Levinson, S. C. (1987). Politeness: Some Universals in Language Usage (Vol. 4). Cambridge University Press. https://doi.org/10.1017/CBO9780511813085

Cristofaro, M. D. (2023). Corpus Approaches to Language in Social Media (1st edn). Routledge. https://doi.org/10.4324/9781003225218

Daoudi, A. (2011). Globalization, Computer-mediated Communications and the Rise of e-Arabic. Middle East Journal of Culture and Communication, 4(2), 146–163. https://doi.org/10.1163/187398611X571328

Derks, D., Bos, A. E. R., & von Grumbkow, J. (2008). Emoticons and Online Message Interpretation. Social Science Computer Review, 26(3), 379–388. https://doi.org/10.1177/0894439307311611

djeghri zarouta, nassira. (2025). The Use of Arabic language on social media: Between Diffusion and Hybridization -Descriptive and Analytical Study. المفكر, 9(2), 635–666.

Doughan, Z., Itani, S., & Itani, S. (2025). ArabSis: Arabic Corpus Sentiment Analysis. IEEE Access, 13, 81083–81095. https://doi.org/10.1109/ACCESS.2025.3567755

Hamdan, H. J. (2021). The Use of Arabic Neologisms in Social Media Applications. International Journal of Arabic-English Studies, 21(1). https://doi.org/10.33806/ijaes2000.21.1.3

Hanadi, A. A., Loureiro-Rodríguez, V., & Fidan Acar, E. (2024). Makkan Arabic in the digital age: A sociolinguistic analysis of the representation of fricative, stop, and sibilant variation in WhatsApp text messages. Journal of Arabic Sociolinguistics, 2(2), 135–154. https://doi.org/10.3366/arabic.2024.0029

Kasmaluddin. (2026). Duolingo-Based Arabic Vocabulary Acquisition in Islamic Educational Contexts. Al-Muhawaroh: Jurnal Pendidikan Bahasa Arab, 2(1), 1–11. https://doi.org/10.38073/almuhawaroh.v2i1.4358

Kemp, S. (2025). TikTok Users, Stats, Data, Trends, and More. DataReportal – Global Digital Insights. https://datareportal.com/essential-tiktok-stats

La Rosa, C. (2022). Linguistic Varieties and Social Radios in the Tunisian Sahel The Case of News Broadcasting: The Case of News Broadcasting. Annali di Ca’ Foscari. Serie orientale, (1), JournalArticle_8297. https://doi.org/10.30687/AnnOr/2385-3042/2022/01/004

Lazuardi, A. M., Baharun, S., Solehudin, M., & Husein, A. F. (2026). Enhancing Arabic Listening Comprehension Skills through YouTube-Based Learning: A Community Service Program at Ribath Imam Al-Ghazali. Community, Innovation, Education, and Empowerment, 1(1), 1–8.

Leblebici, D., & Rostom, M. (2025). “Alexa learned Arabic”: A translanguaging and multimodal perspective on language and media ideologies. Discourse, Context & Media, 66, 100909. https://doi.org/10.1016/j.dcm.2025.100909

Mohamed, A., & Zaghouani, W. (2024). Expression of Depression Among Arab Twitter Users Using Arabic Corpus Analysis. Procedia Computer Science, 244, 76–85. https://doi.org/10.1016/j.procs.2024.10.180

Muh sabilar rosyad, Muhammad Afifuddin, & Muhammad Afthon Ulin Nuha. (2023). HYBRID TRANSLATION: TINJAUAN MEDIA PENERJEMAHAN INDONESIA-ARAB BERBASIS OFFLINE DAN ONLINE PERSPEKTIF MAHASISWA PENDIDIKAN BAHASA ARAB. JALIE: Journal of Applied Linguistics and Islamic Education, 7(02), 225–248.

Mukhasibi, A., & Wahyudi, I. (2026). Immersive Arabic Islamic Environment and Student Engagement: Evaluating the Linguistic Impact of World Arabic Language. Farasyah: Journal of Linguistics and Language Education, 1(1), 1–12.

Nabilah, N., Mufidah, H. N., Nafisah, S., Baihaqi, M., Ulin Nuha, A. N., & Sa’adi, Z. R. (2025). The Influence of Social Media on Arabic Language Change in Online Communication. Arabi : Journal of Arabic Studies, 10(1), 14–25. https://doi.org/10.24865/ajas.v10i1.753

Nguyen, H., & Diederich, M. (2023). Facilitating knowledge construction in informal learning: A study of TikTok scientific, educational videos. Computers & Education, 205, 104896. https://doi.org/10.1016/j.compedu.2023.104896

Sadeghi, R., Akbari, A., & Jaziriyan, M. M. (2024). ExaAUAC: Arabic Twitter user age prediction corpus based on language and metadata features. Discover Artificial Intelligence, 4(1), 48. https://doi.org/10.1007/s44163-024-00145-0

Sapawi, M. S. M., & Yusoff, N. M. R. N. (2025). Integrating technology into the Arabic language curriculum: A systematic review of trends, strategies and cultural dimensions. Social Sciences & Humanities Open, 12, 101974. https://doi.org/10.1016/j.ssaho.2025.101974

Sarbazi-Azad, S., Akbari, A., & Khazeni, M. (2021). ExaAEC: A New Multi-label Emotion Classification Corpus in Arabic Tweets. 2021 11th International Conference on Computer Engineering and Knowledge (ICCKE), 465–470. https://doi.org/10.1109/ICCKE54056.2021.9721493

Seyidov, R., & Çitil, A. (2024). The Impacts of Contemporary Educational Technologies on Learning Arabic. EVOLUTIONARY STUDIES IN IMAGINATIVE CULTURE, 81–92. https://doi.org/10.70082/esiculture.vi.2131

Tarihoran, N., Fachriyah, E., Tressyalina, & Sumirat, I. R. (2022). The Impact of Social Media on the Use of Code Mixing by Generation Z. International Journal of Interactive Mobile Technologies (iJIM), 16(07), 54–69. https://doi.org/10.3991/ijim.v16i07.27659

Walther, J. B. (1992). Interpersonal Effects in Computer-Mediated Interaction: A Relational Perspective. Communication Research, 19(1), 52–90. https://doi.org/10.1177/009365092019001003

Zappavigna, M., & Logi, L. (2024). Emoji and Social Media Paralanguage (1st edn). Cambridge University Press. https://doi.org/10.1017/9781009179829