OKARA: Jurnal Bahasa dan Sastra

Morphosyntactic Adaptation and Functional Use of English Loanwords in Gen Z Online Service Conversations

2026-06-27T11:30:24+07:00

The increased prevalence of English in daily Indonesian communication was attributed to the rise of digital commerce and social media. Unlike previous research on code-mixing and borrowing, few studies have investigated how English words were modified in online service chats. This paper investigated how Indonesian Gen Z users transformed English words morphosyntactically in their conversations. It analyzed 124 chat interactions collected from Shopee, Tokopedia, Instagram, and WhatsApp. Using qualitative analysis, the study identified recurring morphosyntactic processes, including clipping, affixation, permutation, and hybrid command formation. Examples included "notif” (from notification), “orderan” (order + -an), “di-scan” (scan with the Indonesian passive prefix di-), and “auto-panik” (automatically panic). These forms showed that English words were restructured to fit Indonesian linguistic patterns, becoming integrated into everyday digital communication rather than remaining isolated loanwords. Interview data revealed that users employed these forms to make communication more efficient, distinctive, humorous, and socially engaging. The findings confirmed that Indonesian youth actively modified English words to support efficient, creative, humorous, and interactive communication. These usage patterns contributed to ongoing changes in Indonesian digital communication.

Exploring the Language of Flavor: Emotive Language Analysis of Culinary Tourism Promotion in Solo City

2026-06-27T11:30:25+07:00

This study presents a meticulous examination of evaluative language patterns in culinary promotional texts, focusing on the emotive dimensions of communication within the context of Solo City’s culinary tourism discourse. Through a systematic analysis of culinary and restaurant review texts, distinct evaluation patterns and linguistic phenomena are identified and scrutinized. The analysis reveals a predominant emphasis on APPRECIATION aspects, particularly REACTION:IMPACT and REACTION:QUALITY, in conveying evaluations towards culinary entities, highlighting the strategic use of emotive language to captivate reader interest and engagement. However, there exists a notable underutilization of VALUATION aspects, which diminishes the comprehensiveness of the evaluative discourse. Furthermore, the absence of AFFECT and JUDGMENT aspects represents a missed opportunity to enrich the evaluative narrative and build familiarity with readers. Authors adeptly navigate the terrain of evaluative language, employing ATTITUDE aspects such as INTENSIFIERS and ATTITUDINAL LEXIS to imbue their discourse with emotive depth and foster reader engagement. The findings underscore the need for a balanced evaluation approach integrating APPRECIATION, VALUATION, AFFECT, and JUDGMENT aspects to sustain reader interest effectively. Future research directions include exploring cultural influences on evaluative language and investigating linguistic strategies’ effectiveness in culinary promotional texts.

Linguistic Fingerprints for Authorship Attribution in Human–AI Collaborative Texts

2026-07-13T15:36:24+07:00

Authors leave distinctive linguistic traces that reflect their identity through consistent writing styles, particularly in morphosyntactic patterns and lexical choices. However, the increasing use of AI writing tools challenges authorship attribution because machine-generated text can imitate or obscure individual writing characteristics. This study investigates linguistic features that effectively identify authors and differentiate human-written from AI-generated texts. A corpus comprising 2,074,125 tokens and 63,414 word types was compiled from collaborative digital platforms, including instant messaging and social media. Lexical and stylistic features were extracted to develop hybrid authorship-classification models, while N-gram tracing was used to identify salient patterns. The findings demonstrate that lexical choice is the most reliable indicator for distinguishing human and AI-generated texts. Character-level N-gram analysis further demonstrates that authorship can be identified through delicate patterns involving letters, capitalization, punctuation, and other non-alphabetic characters. Diction appeared as the strongest factor in differentiating individual authors. These results enhance the reliability of authorship attribution methods and provide valuable insights for forensic investigations of digitally mediated communication involving human–AI interaction.