L'Geneus : The Journal Language Generations of Intellectual Society

Large Language Models and the Future of Computational Linguistics

2026-05-30T09:42:54+00:00

The emergence of Large Language Models (LLMs) has marked a significant milestone in the development of Artificial Intelligence (AI) and Natural Language Processing (NLP), profoundly influencing the field of computational linguistics and transforming the way language is analyzed, processed, and generated. This study aims to examine the impact of LLMs on the future development of computational linguistics by exploring their contributions, limitations, and broader implications for language research and technology. The study employs a qualitative literature review approach, incorporating elements of systematic literature review and conceptual analysis to synthesize findings from scholarly publications, conference proceedings, and industry reports related to LLMs and computational linguistics. The results indicate that LLMs have substantially enhanced language modeling, machine translation, text generation, discourse analysis, language documentation, and multilingual processing, while also expanding opportunities for linguistic research and AI-driven language applications. Furthermore, the findings reveal that LLMs outperform many traditional NLP approaches through their ability to capture complex syntactic, semantic, and contextual relationships. However, challenges such as hallucination, bias, limited explainability, privacy concerns, and ethical issues continue to affect their reliability and responsible use. In conclusion, LLMs represent a transformative force in computational linguistics, offering unprecedented opportunities for innovation and language technology development while simultaneously introducing important technical, ethical, and societal challenges. The future of computational linguistics is likely to involve deeper integration of AI-driven methodologies supported by continued human expertise, interdisciplinary collaboration, and the development of more transparent, fair, and linguistically informed language models.

Critical Discourse Analysis of Artificial Intelligence Narratives in Online Media

2026-05-28T08:24:09+00:00

The rapid development of artificial intelligence (AI) technologies has significantly increased the presence of AI-related narratives in online media, where digital platforms play an important role in shaping public perceptions, attitudes, and ideological understandings of technological innovation. This study aims to critically analyze how artificial intelligence is represented and framed within online media discourse and to uncover the ideological meanings embedded in AI-related narratives. The research employed a qualitative approach using a descriptive-critical design through the framework of Critical Discourse Analysis (CDA). The data consisted of online news articles, blogs, social media posts, and AI-related digital publications collected from various online platforms published between 2020 and 2026. The analysis applied Norman Fairclough’s three-dimensional CDA model, including text analysis, discursive practice, and social practice. The findings revealed several dominant representations of AI in online media, including AI as technological progress, economic opportunity, human replacement, ethical threat, and futuristic solution. The study also identified dominant ideological patterns such as techno-optimism, fear-based ideology, commercialization ideology, and ethical awareness reflected through lexical choices, metaphors, headlines, emotional language, and framing strategies. Furthermore, the findings demonstrated that online media discourse significantly influences public understanding of artificial intelligence by shaping trust, social anxiety, labor concerns, and ethical perspectives regarding technological development. Overall, this study concludes that online media functions not only as a channel of information dissemination but also as a powerful discursive institution that constructs ideological meanings and public perceptions of artificial intelligence through strategic language use and media framing.

A Forensic Linguistic Investigation of Deceptive Communication in WhatsApp-Based Fraud

2026-06-02T13:23:33+00:00

The increasing use of digital communication platforms, particularly WhatsApp, has been accompanied by a growing number of online fraud cases that exploit messaging applications as a medium for deception and manipulation. As fraudsters increasingly rely on language to establish credibility, influence victims, and conceal fraudulent intentions, forensic linguistics has become a valuable approach for examining linguistic evidence within digital communication. This study aims to analyze the linguistic patterns and communicative strategies employed in WhatsApp chat-based fraud in order to identify indicators of deceptive communication. The research adopts a qualitative forensic linguistic approach using WhatsApp fraud chat transcripts collected from victim reports, archived scam conversations, and publicly available fraud documentation as the primary data source. The data were analyzed through lexical, syntactic, pragmatic, and discourse analysis to examine how language is strategically used by fraudsters. The findings reveal that fraudulent conversations are characterized by recurring lexical features such as urgency-related vocabulary, authority claims, security-related terminology, and reward-based expressions. Syntactically, fraudsters frequently employ imperative and interrogative structures to direct victim behavior and obtain sensitive information. Pragmatic analysis indicates the extensive use of directive, assertive, and commissive speech acts, as well as implicatures and persuasive strategies designed to create trust and encourage compliance. At the discourse level, fraudulent interactions typically follow structured stages involving introduction, trust-building, information gathering, persuasion, and execution, often supported by narrative manipulation and false identity construction. The study concludes that WhatsApp-based fraud exhibits identifiable linguistic characteristics that function as indicators of deception and criminal intent. These findings demonstrate the significant role of forensic linguistic analysis in detecting fraudulent communication, supporting digital forensic investigations, and contributing to cybercrime prevention efforts through increased public awareness and the development of language-based fraud detection systems.

Pragmatic Analysis of User Interaction with AI Chatbots in Digital Services

2026-05-29T09:14:38+00:00

The rapid development of artificial intelligence technology has significantly transformed digital communication through the increasing use of AI chatbots in various sectors such as e-commerce, banking, healthcare, education, customer service, and government digital services. As human interaction with AI systems becomes more common, the effectiveness of chatbot communication has become an important issue, particularly in relation to contextual understanding, implied meaning, and conversational appropriateness. This study aims to analyze the pragmatic aspects of user interaction with AI chatbots in digital services, focusing on speech acts, contextual understanding, conversational implicature, politeness strategies, and communication effectiveness. The study employs a qualitative descriptive approach using discourse analysis and a pragmatic analysis framework. Data were collected from chatbot-user conversations across various digital platforms through observation, documentation, screenshot collection, and conversation transcription. The analysis is based on pragmatic theories including Speech Act Theory by John Searle, the Cooperative Principle and conversational implicature by H. P. Grice, and Politeness Theory by Penelope Brown and Stephen Levinson. The findings reveal that AI chatbots are generally capable of performing basic pragmatic functions effectively, particularly in structured and direct interactions involving information delivery and procedural guidance. However, chatbots still experience limitations in interpreting indirect requests, implied meanings, emotional nuances, sarcasm, and complex contextual relationships. Communication failures frequently occur when users employ implicit or emotionally sensitive expressions that require deeper pragmatic inference. Therefore, improving contextual awareness, emotional sensitivity, and inferential reasoning is essential for developing more natural, adaptive, and human-centered conversational AI systems in digital services.

Digital Literary Analysis of AI-Generated Stories: Narrative Structure, Characterization, Themes, and Creativity in Computational Storytelling

2026-06-03T10:08:42+00:00

The rapid development of artificial intelligence and generative language technologies has contributed to the emergence of AI-generated literary texts as a significant phenomenon within contemporary digital literature. As AI systems increasingly participate in creative writing, questions regarding narrative construction, literary quality, creativity, and authorship have become important areas of scholarly investigation. This study aims to analyze the literary characteristics of AI-generated stories through a digital literary perspective, focusing on narrative structure, characterization, themes, language, style, and creativity. The research employed a qualitative approach using digital literary analysis, with AI-generated narratives collected from contemporary generative AI platforms as the primary data source. The findings reveal that AI-generated stories generally demonstrate coherent narrative structures characterized by clear beginnings, middles, and endings, logical plot progression, and identifiable climaxes and resolutions. In terms of characterization, the stories contain consistent protagonists and supporting characters, although they often lack deep emotional and psychological complexity. Linguistically, the stories exhibit grammatical fluency, rich vocabulary, and effective descriptive imagery; however, they frequently rely on conventional metaphors, repetitive expressions, and familiar stylistic patterns. Regarding creativity, AI-generated narratives demonstrate a strong ability to reproduce established literary conventions and narrative structures but show limitations in originality, innovation, and profound artistic expression. Overall, the study concludes that AI-generated storytelling possesses considerable literary potential as an emerging form of digital literature, particularly in terms of narrative coherence and linguistic competence, yet remains constrained by limitations in emotional depth, stylistic originality, and creative complexity when compared to highly sophisticated human-authored literary works.