International Journal of Innovative Technology and Interdisciplinary Sciences

Tracking Topic Drift in AI and Workforce Narratives on Reddit: A Longitudinal Visualization from 2010–2024

2025-11-14T20:26:34+01:00

This article extends previous research on Reddit-based discourse of artificial intelligence (AI) and workforce automation by adding a longitudinal analysis of thematic evolution. With a sample of 4,243 Reddit posts between 2010 and 2024, we examine how attention has evolved over time between top AI-related topics of work, reskilling, regulation, and use cases such as ChatGPT. We apply Latent Dirichlet Allocation (LDA) topic modelling for the research and use advanced visualization tools like word clouds, heatmaps, and time-series plots to estimate topic drift. Our findings indicate that while worries over job loss and ethical governance persist, discussions over certifications and upskilling have augmented steadily. Above all, interest in generative AI started to rise steeply after 2022, indicating a remarkable change in people's opinions. These results represent the development of societal problems and technological awareness over time. This research proved helpful by using the prevalence of topics for policymakers, educators, and industry players who need to understand the changed public dis-course in the role of AI within the labour market. In this respect, it points out the increasing necessity for adaptive skills strategies and evidence-based communication.

A Data-Driven Survival Analysis of Prognostic Determinants in Patients with Alcohol-Related Liver Disease: A Prospective Study

2025-11-12T20:22:06+01:00

Alcohol-related liver disease (ALD) is a leading cause of liver-related mortality in Europe, yet prospective survival data from Southeast Europe remain limited. Prognostic assessment has traditionally focused on biological disease severity, while behavioral factors particularly sustained alcohol abstinence is less consistently incorporated. It has been conducted a prospective observational cohort study of 200 adults with confirmed ALD treated at a national tertiary referral center in Albania and followed for 12 months. Sustained alcohol abstinence (≥6 months) was modelled dynamically as a time-varying exposure within an integrated biological–behavioral prognostic framework. Overall survival was evaluated using Kaplan–Meier analysis and Cox proportional hazards models, with liver transplantation treated as a censoring event; competing-risk models were applied to account for transplantation as a competing outcome. During follow-up, 44 patients (22%) died. Non-survivors had significantly higher Model for End-Stage Liver Disease (MELD) scores (21.0 ± 7.1 vs. 15.0 ± 6.2, p < 0.001) and a higher prevalence of ascites (77% vs. 46%, p = 0.002) and hepatic encephalopathy (52% vs. 19%, p < 0.001). Sustained abstinence was less frequent among non-survivors (20% vs. 46%, p = 0.013) and was associated with improved survival (log-rank p = 0.013). In multivariable Cox and competing-risk analyses, MELD, ascites, and hepatic encephalopathy independently predicted mortality, whereas time-varying abstinence demonstrated an independent protective effect. The combined biological–behavioral model showed good discrimination and calibration (optimism-corrected Harrell’s C-index 0.78–0.82; 12-month AUC ≈ 0.80). In this underrepresented Southeast European cohort, established severity markers remained dominant predictors of short-term mortality, while the dynamic incorporation of abstinence provided incremental prognostic value, supporting improved risk stratification and pragmatic ALD management in resource-limited settings.

Charting the Digital Frontier: A Comprehensive Bibliometric Analysis of E-Agriculture Research

2025-10-24T14:44:52+02:00

This study presents a statistically validated bibliometric analysis of e-agriculture research published between 2020 and 2025, based on 1,363 peer-reviewed articles indexed in Scopus and Web of Science, and selected according to the PRISMA 2020 guidelines. Bibliometric mapping is combined with inferential statistical analysis and network validation to examine publication dynamics, thematic evolution, citation impact, and global collaboration patterns. Results show rapid growth in research output up to 2023, followed by a contraction in 2024. Core research themes include smart farming, Internet of Things (IoT), artificial intelligence particularly deep learning and precision agriculture. While China, India, and Brazil lead in publication volume, the United States, the Netherlands, and Germany exhibit higher citation impact, indicating a divergence between productivity and influence. Inferential testing confirms these patterns: one-way ANOVA reveals significant temporal variation in citation impact (F(5,1357)=48.5, p<2×10⁻¹⁶), and network modularity analysis (Q=0.519) demonstrates a robust thematic structure. Poisson regression further shows that publication year and thematic focus jointly shape citation performance. To extend beyond descriptive bibliometrics, the study integrates an altmetric perspective, drawing on Twitter sentiment and topic analysis to capture societal engagement with digital agriculture research. Overall, the study advances bibliometric analysis in e-agriculture by combining statistical validation, network robustness assessment, and signals of societal impact.