Jul 2026· Aposta: Revista de Ciencias Sociales· Vol 24, pp. e1522· 0 citations· 20 references
TL;DR
The findings reveal that generative AI accelerates lexical innovation through the emergence of AI-related terminology, promotes the diffusion of newly coined lexical items across digital platforms, and contributes to semantic expansion and diverse word-formation processes.
Abstract
Generative artificial intelligence (AI) has rapidly emerged as a transformative force in digital communication, reshaping contemporary language use and accelerating lexical change across online environments. A growing number of empirical studies have examined various aspects of generative AI, including AI-assisted writing, digital discourse, language use, and computational linguistics, thereby, limited research has systematically synthesized how generative AI influences lexical change in digital discourse from a linguistic perspective. The present study aims to systematically review empirical linguistic research published between January 2023 and May 2026 to examine the influence of generative AI on lexical change, identify recurring patterns of lexical innovation, and synthesize the existing body of evidence. Guided by the PRISMA 2020 framework, the review analyzes 59 empirical studies collected from peer-reviewed journals indexed in the Web of Science Core Collection (WoSCC) using descriptive analysis and thematic synthesis. The findings reveal that generative AI accelerates lexical innovation through the emergence of AI-related terminology, promotes the diffusion of newly coined lexical items across digital platforms, and contributes to semantic expansion and diverse word-formation processes. Additionally, the review identifies recurring linguistic patterns that demonstrate the evolving relationship between AI technologies and contemporary digital communication. Future research should expand empirical investigations to multilingual digital environments and longitudinal contexts to develop a more comprehensive understanding of AI-driven lexical change across diverse linguistic communities.
This study integrates bibliometric visualization with content coding to clarify the development, knowledge structure, and research gaps of digital discourse studies, offering directions for future interdisciplinary inquiry.
Yifan Liu, Omar Ali Al-Smadi, Siti Soraya Lin Abdullah Kamal et al.· Journal of Nusantara Studies...· 0 citations
The review concludes that the future of applied linguistics depends on developing human-centered and interdisciplinary approaches that balance technological innovation with human agency, linguistic diversity, educational quality, and ethical responsibility in increasingly AI-mediated language environments.
A. Mohammed, Al Fallah, Al Karmaji· Comprehensive Journal of Sci...· 0 citations
The rapid mainstream adoption of generative artificial intelligence (AI) has triggered intense and deeply polarized global discourse, frequently divided between systemic optimism (“hype”) and existential anxiety (“doom”). While contemporary literature predominantly arranges public perceptions of AI along static anthropomorphic gradients, empirical examinations analyzing digital discourse through the processual, dynamic lens of Cognitive Linguistics remain critically scarce. To address this gap, this study adopts a mixed-methods design integrating quantitative Corpus Linguistics with the interpretative depth of Critical Metaphor Analysis (CMA). By systematically identifying Peña Cervel’s (2003, 2012) foundational image-schematic configurations serving as primary master domains, this research investigates how these conceptual building blocks integrate into the macro-framework of Lakoff’s Event Structure Metaphor (ESM). It further extends this cognitive mapping into Musolff’s (2016) discourse scenarios to demonstrate how underlying embodied and socio-cognitive structures culminate in culturally conditioned digital discourse. Utilizing a compiled corpus of 5,000 Turkish YouTube comments (2019–2025) as a critical case study, our primary objective is to unveil the deep cognitive infrastructure through which everyday social actors conceptualize AI’s developmental trajectory. The findings reveal that rather than perceiving AI merely as a static agent, public discourse relies heavily on force-dynamic configurations—framing AI as an unfolding event structured by metaphorical paths, motion dynamics, and systemic barriers. By bridging corpus-driven frequencies with critical discourse dimensions, this study demonstrates how metaphorical frameworks actively license contrasting ideological positions and structure collective perceptions of AI as a socio-technological force.
E. Esmer, Şaziye Yaman· SN Social Sciences· 0 citations
The rapid growth of generative AI has generated excitement about its potential and concern about its effects on psychological research. Schmitt, Hao, Pham, and Hofstetter (2026) offer Generative Grounded Theory (GGT) as a seven‐step framework for incorporating generative artificial intelligence into inductive theory building. It uses AI to support corpus formation, data structuring, coding, conceptual clustering, abstraction, theoretical integration, and the assessment of saturation, while reserving interpretive authority and theoretical responsibility for the researcher. These Commentaries of this Methods Dialogue respond to the accepted, revised version of the lead article by Schmitt et al. (2026), which incorporated feedback from open, collaborative reviews by established researchers. Following acceptance, the review teams provided four independent assessments of the value of GGT. Tomaino proposes that GGT is particularly suited for experimental researchers who are generating rich conversational data through AI‐mediated studies, as GGT can possibly reveal mechanisms, generate new research questions, and unearth competing explanations. Schweidel emphasizes the researcher's indispensable role as a “cognitive operator.” He acknowledges that GGT's traceability facilitates replication, digital‐twin exploration, and coordination across qualitative–quantitative researchers, but only if researchers resist sycophancy, cognitive offloading, and the temptation to delegate the entire process to an autonomous agent. Dolbec, Fischer, and Smith expand the focus on the responsibility of the GGT researcher, arguing that the ease of AI‐assisted analysis may create a “fallacy of facility” in which plausible output is mistaken for expertise. They propose that methodological simulacrums can produce polished but weakly grounded research unless investigators actively harness substantial qualitative method expertise to control AI's distorting capability. Finally, Puntoni and Schillewaert shift attention upstream from analysis to data collection. They argue that using AI moderation may relax the long‐standing separation between qualitative depth interviews and standardized variable assessment. If so, AI‐adaptive interviews could integrate sampling and analysis by offering a “living” grounded theory, while raising new questions about saturation and reproducibility. Together, these commentaries acknowledge that GGT could extend the reach of inductive inquiry and facilitate automated theorizing, but it is a method that demands critical judgment, transparent record‐keeping, and human control.
Geoff Tomaino, D. Schweidel, P. Dolbec et al.· Journal of Consumer Psycholo...· 0 citations
Drawing on genre analysis and corpus-based translation studies, this study examines rhetorical hype in Chinese government White Papers published between 2020 and 2023 and their official English translations. Rhetorical hype is defined as evaluative and promotional language that heightens the perceived importance, novelty, effectiveness, scope, or desirability of policies, actions, knowledge claims, and accomplishments, with its individual lexical realizations termed hyperbolic items.
Using a sentence-level move scheme adapted from previous genre studies, the study identifies seven move types and focuses on the three most extensive moves: Detailing Actions, Presenting Epistemic Knowledge, and Presenting Accomplishments. Hyperbolic items were identified using a candidate lexicon compiled from previous studies and lexical resources, followed by contextual validation and examination of aligned source–target units. Descriptive frequency analysis was combined with qualitative analysis of translation patterns across the three moves.
The English translations contain fewer hyperbolic items overall than the Chinese source texts. Presenting Accomplishments exhibits the highest normalized frequency of hyperbolic items in both corpora, whereas the relative distribution of such items in Detailing Actions and Presenting Epistemic Knowledge differs between the source and target texts. Qualitative analysis reveals three major translation patterns—attenuation, retention, and amplification—with attenuation predominating in the sampled data.
The findings indicate that the English translations display a more restrained evaluative profile than the Chinese source texts, a pattern compatible with target-oriented institutional mediation. By integrating rhetorical move analysis with the analysis of evaluative language, this study offers a more context-sensitive account of rhetorical hype in political discourse translation and demonstrates the value of combining genre-based and corpus-based approaches to institutional translation.
Luo-Jia Wang· Frontiers in Communication· 0 citations
A statistically significant increase in lexical density and frequency of cohesive markers has been revealed, indicating an increase in information compression and explicit discursive organization of texts, consistent with characteristics described in AI-assisted writing studies.
T. Nedashkivska, I. Varvaruk, M. Podoliak et al.· Journal of Intelligent Decis...· 0 citations
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