Volume 5 Issue 2: June 2026 (in progress)

The use of algorithmic decision systems is being expanded to high-risk areas like credit, recruiting, and distributing government resources. Despite the fact that these systems are usually claimed to be objective and efficient, there have been apprehensions about the likelihood of structural inequalities being perpetuated by the systems. This paper examines the effect of a fairness-aware pre-processing technique called reweighing on the performance of a predictive system in a controlled simulation environment. Using a synthetically created credit approval dataset with structural disadvantage embedded, we compare the performance of a logistic regression classifier with and without reweighing. Fairness is measured using demographic parity disparity (DPD), disparate impact ratio (DIR), and equalized odds difference (EO), along with predictive accuracy. In a single test scenario (seed = 42), reweighing does not improve all fairness metrics uniformly. However, when analyzed for robustness across 50 independent random seeds, we find modest average reductions in demographic parity disparity and equalized odds difference for reweighing, with little change in predictive accuracy. Threshold sensitivity analysis also shows that fairness metrics are sensitive to decision thresholds. These results show that fairness-aware pre-processing can lead to systematic improvements in expectation, although trade-offs across fairness metrics and performance remain context-dependent.

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This study investigates how three personality traits from the Big Five framework—extraversion, neuroticism, and conscientiousness—influence individuals’ continuance intention to use ChatGPT, with a particular focus on the mediating roles of Perceived Ease of Use (PEOU) and Perceived Usefulness (PU). Drawing on the Technology Acceptance Model (TAM), this research aims to integrate personality-based differences into a well-established framework for understanding technology adoption and sustained usage. Data were collected through an online survey targeting active ChatGPT users and analyzed using structural equation modeling to examine both direct and indirect relationships among variables. The findings indicate that conscientiousness has a strong and positive impact on both PEOU and PU, and indirectly enhances continuance intention through these cognitive evaluations. Extraversion shows a limited but positive effect primarily through perceived ease of use, suggesting that socially oriented individuals may engage with the system when it is easy to navigate. In contrast, neuroticism does not demonstrate any statistically significant relationship with the key variables in the model. Consistent with TAM, PEOU significantly influences PU and continuance intention, with PU emerging as the most influential predictor of sustained usage. Overall, this study highlights the critical role of conscientiousness in fostering long-term engagement with generative AI systems and underscores the importance of cognitive perceptions in mediating personality effects. By integrating personality psychology with technology acceptance theory, the research provides theoretical and practical implications for designing personalized and adaptive AI interfaces.

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This research investigates the dynamic relationship of Artificial Intelligence (AI) and Sociolinguistics through bibliometric mapping in association with keyword content analysis. Utilizing 69 extracted publications (2013–2024) after systematic deduplication, the study combines quantitative trend analysis with keyword-based thematic interpretation. From an initial collection of 98 records obtained from Scopus (n = 64) and Web of Science (n = 34), a subset of 48 publications was sampled further pursuant to their conceptual relevance. Bibliometric analysis with the software ScientoPy and VOSviewer was employed to reveal publication trajectories, top contributors, influential journals, geographic patterns, and knowledge hot spots. This mapping was supplemented with a qualitative examination of the space mapped using five major terms: Computational Sociolinguistics, Natural Language Processing (NLP), ChatGPT, language and machine learning enabling us to track prevalent themes and concepts structuring the field. These results indicate that scholarly interest in the sociolinguistic aspects of AI-mediated communication has grown substantially, especially pertaining to language ideology, identity construction, and algorithmic influence on discourse. Instead of portraying computational methods as passive and neutral tools, the findings imply that technology such as NLP and large language models can be seen as both reproducing and destabilizing linguistic hierarchies, bringing to light critical questions regarding representation, diversity, and equity in digital space. In this work, we map the intersection of AI and Sociolinguistics through a combination of bibliometric mapping and keyword-based interpretation, thus giving an overview of how the field has evolved over time. This finding implies that debates about ethical and culturally inclusive AI design are coalescing into prominence in the literature.

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The paper is located within the dynamic relationship between language and computing, with the central objective being an increase in knowledge about how individuals interact with digitally mediated communication. Precisely, the paper attempts to explore the use of emojis as a tool of the digitally mediated communication system from a linguistic perspective, especially within the pragmatic and socio-linguistic framework among Jordanian university students. The main objectives involve four dimensions: determining the usage of emojis by members of this community, explaining subjects' tendencies towards emojis while conducting online discourse, understanding the meanings of a selected list of emojis derived from the subjects' point of view, and exploring possible gender differences within the Jordanian culture. Methodologically, this work followed the triangulation approach, wherein three research instruments were used: an online survey distributed across the sample population, consisting of 500 Jordanian university students from both genders, corpus analysis; and expert interviews. The sociolinguistic aspect of the analysis revealed that demographic focused on gender as a social variable regarding, some effects on emoji preferences and interpretations among participants. Finally, it contextualizes its findings within the general theoretical framework provided by three functional language, in relation to the phatic, conative, and expressive functions situated within a Jordanian cultural context. It finds the end by placing at the forefront of interest the dire need for interdisciplinary collaborations in exploring digitally mediated communication from linguistic vantages, particularly against a changing landscape where language and technology are crossed.

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