Digital Discourse and Semantic Change
A Diachronic Corpus Analysis of Lexical Meaning Shifts in English Social Media
DOI:
https://doi.org/10.68379/t6h83062Keywords:
Lexical Innovation, Semantic Change, Social Media, Diachronic Corpus Analysis, Digital Discourse, Sociocultural InfluenceAbstract
This paper examines the mechanisms of lexical innovation, diffusion, and stabilization in English social media discourse. The study employs a diachronic corpus analysis of Twitter, Reddit, and Instagram data from 2010 to 2025 to investigate the interplay among digital platforms, sociocultural contexts, and traditional media in the development of new word meanings. To trace semantic changes such as narrowing, broadening, amelioration, and functional shift, the lexical items ghosting, lit, and stan were analyzed. The study addresses three main research questions: What role do social media platforms play in the formation, dissemination, and stabilization of lexical meanings? How do traditional media and linguistic authorities mediate this process? How do social, cultural, and community-specific factors affect lexical adoption? The results indicate that Twitter accelerates diffusion, Reddit promotes semantic negotiation, Instagram reinforces stabilization, and traditional media provides legitimacy. The adoption and retention of lexical items are strongly influenced by sociocultural norms and community practices. This study presents a cross-platform, sociocultural account of digital semantic change, with implications for lexicography, language education, and computational linguistics.
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References
ABIDIN, C., & LEE, J. (2023). TikTok Methodologies.
Androutsopoulos, J. (2014). Mediatization and sociolinguistic change (Vol. 36). Walter de Gruyter GmbH & Co KG. DOI: https://doi.org/10.1515/9783110346831
Baron, N. S. (2010). Always on: Language in an online and mobile world. Oxford University Press.
Barton, D., & Lee, C. (2013). Language online: Investigating digital texts and practices. Routledge.
Bruns, A., Burgess, J. J. H. p. T. p., & networks, p. o. d. (2015). Twitter hashtags from ad hoc to calculated publics. 103, 13-28.
Bucher, T., & Helmond, A. J. T. S. h. o. s. m. (2018). The affordances of social media platforms. 1(1), 233-253. DOI: https://doi.org/10.4135/9781473984066.n14
Crystal, D. (2011). Internet linguistics: A student guide. Routledge. DOI: https://doi.org/10.4324/9780203830901
Eisenstein, J., O'Connor, B., Smith, N. A., & Xing, E. P. J. P. o. (2014). Diffusion of lexical change in social media. 9(11), e113114. DOI: https://doi.org/10.1371/journal.pone.0113114
Grieve, J., Nini, A., Guo, D. J. E. L., & Linguistics. (2017). Analyzing lexical emergence in Modern American English online1. 21(1), 99-127. DOI: https://doi.org/10.1017/S1360674316000113
Hamilton, W. L., Leskovec, J., & Jurafsky, D. (2016). Diachronic word embeddings reveal statistical laws of semantic change. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), DOI: https://doi.org/10.18653/v1/P16-1141
Herring, S. C. J. D. (2013). Discourse in Web 2.0: Familiar, reconfigured, and emergent. 2(0), 1-26.
Hutchby, I. J. S. (2001). Technologies, texts and affordances. 35(2), 441-456. DOI: https://doi.org/10.1177/S0038038501000219
Jatowt, A., & Duh, K. (2014). A framework for analyzing semantic change of words across time. IEEE/ACM joint conference on digital libraries, DOI: https://doi.org/10.1109/JCDL.2014.6970173
Kershaw, D. J. (2018). Language change and evolution in online social networks. Lancaster University (United Kingdom).
Kutuzov, A., & Giulianelli, M. (2020). UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. Proceedings of the fourteenth workshop on semantic evaluation, DOI: https://doi.org/10.18653/v1/2020.semeval-1.14
Lenci, A. J. A. r. o. L. (2018). Distributional models of word meaning. 4, 151-171. DOI: https://doi.org/10.1146/annurev-linguistics-030514-125254
McEnery, T., & Hardie, A. (2011). Corpus linguistics: Method, theory and practice. Cambridge University Press. DOI: https://doi.org/10.1017/CBO9780511981395
Nashrudina, P. G. G., Fajriyah, A. M. a., & Dewi, T. I. J. C. N. (2025). The Role of TikTok in Shaping Generation Z’s Slang: Semantic Change and Language Use in Digital Communication. 2(3), 146-156. DOI: https://doi.org/10.59066/cn.v2i3.1005
Nguyen, D., Doğruöz, A. S., Rosé, C., & De Jong, F. J. C. l. (2016). Computational sociolinguistics: A survey. 42(3), 537-593. DOI: https://doi.org/10.1162/COLI_a_00258
Page, R., Barton, D., Lee, C., Unger, J. W., & Zappavigna, M. (2022). Researching language and social media: A student guide. Routledge. DOI: https://doi.org/10.4324/9781003121763
Raza, A., Alvi, U. F., A Aldaghri, A., & Alshraah, S. M. (2025). Gendered Language in English-Medium Professional Digital Discourse: A Corpus-Based Study of Achievement Narratives and Interaction Dynamics on LinkedIn. DOI: https://doi.org/10.2139/ssrn.5743506
RAZA, A., SHAHROZ, M., & IRAM, A. (2024). Argumentation and AI: Bridging linguistic and technological perspectives in education. Contemporary Journal of Social Science Review, 2(04), 1679-1690.
Tagliamonte, S. (2016). Teen talk: The language of adolescents. Cambridge University Press. DOI: https://doi.org/10.1017/CBO9781139583800
Tahmasebi, N., Borin, L., & Jatowt, A. J. a. p. a. (2018). Survey of computational approaches to lexical semantic change.
Tarrade, L., Magué, J.-P., Chevrot, J.-P. J. P. o. L., & Communication. (2022). Detecting and categorising lexical innovations in a corpus of tweets. 26(1), 313-329. DOI: https://doi.org/10.2478/plc-2022-15
TUTOVA, E., EBZEEVA, Y., SMIRNOVA, Y., & GISHKAEVA, L. J. T. T., Psychometrics, Methodology in Applied Psychology. (2025). TECHNOLOGY-DRIVEN LANGUAGE CONTACT: THE DIGITAL EVOLUTION OF LINGUISTIC INTERACTION. 32(S7 (2025): Posted 10 October), 2663-2668.
Wang, Z., Wu, J., Wang, Y., Wang, W., Yang, J., Johnson, J., Sastry, N., & De, S. (2024). Revealing COVID-19’s Social Dynamics: Diachronic Semantic Analysis of Vaccine and Symptom Discourse on Twitter. Findings of the Association for Computational Linguistics: EMNLP 2024, DOI: https://doi.org/10.18653/v1/2024.findings-emnlp.193
Würschinger, Q. J. F. i. A. I. (2021). Social networks of lexical innovation. Investigating the social dynamics of diffusion of neologisms on Twitter. 4, 648583. DOI: https://doi.org/10.3389/frai.2021.648583
Zulli, D., Zulli, D. J. J. N. m., & society. (2022). Extending the Internet meme: Conceptualizing technological mimesis and imitation publics on the TikTok platform. 24(8), 1872-1890. DOI: https://doi.org/10.1177/1461444820983603
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Copyright (c) 2026 Riba Aslam , Arooj Fatima , Muhammad Haseeb Sajjad , Ahmad Raza (Corresponding Author) (Author)
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