Search Results (148)

In 19th-century Hungary, the German-language press provided an important intellectual platform for Jewish journalists, many of whom became significant actors in political communication and cultural transfer. This study examines the Pester Lloyd (founded in 1854) as an institutional case through which the mediating role of Jewish journalists in a multilingual press culture can be explored. By analyzing editorial strategies, journalistic networks, and forms of cultural mediation, the paper explores how Jewish intellectuals operated between Hungarian- and German-language cultural and political spheres. Methodologically, the study combines historical press research with institutional and intellectual–historical analysis, drawing on editorials, feuilletons, autobiographical writings, and contemporary press material. Special attention is given to Miksa Falk, whose editorial leadership embodied the ideals of liberal constitutionalism and linguistic and cultural mediation. The article situates the Pester Lloyd as both a product of and an important medium for journalistic modernization and the participation of Jewish journalists within Hungary’s evolving media landscape. Full article

This article examines the extent to which it is possible to reconstruct part of the technical knowledge employed in pre-Hispanic Andean constructions. Drawing on chronicles, colonial representations, linguistic studies, ethnography and direct architectural analysis, it demonstrates that Andean societies possessed specialised trades and a wider repertoire of tools than is commonly recognised. A central finding is the identification of gaveras—wooden moulds for making adobe bricks—whose recognition here shows that carpentry techniques were more developed than has been assumed. The poor documentation of these objects—several of them miscatalogued in Peruvian museums—has reinforced the mistaken assumption that complex construction tools were not used in ancient Peru. The study combines three approaches: direct observation of the built fabric (marks, imprints and textures that reveal construction processes), review of the Quechua vocabulary associated with tools and trades—drawn from three sixteenth- and seventeenth-century dictionaries consulted in the Langas repository of the CNRS—and comparison with current practices in Andean communities. This is complemented by a comparison with records from other cultures and a systematic search of the database of the Ministry of Culture of Peru, where plumb bobs, floats, shovels, axes, chisels and other instruments rarely analysed from a constructive perspective were located. Taken together, the article demonstrates that pre-Hispanic architecture was neither intuitive nor improvised, but the outcome of a technical tradition carried out by specialists, tools that are little known today, and construction methods based on measurement, alignment control, mortar transfer and careful surface finishing. Full article

Fake news detection has become a major research topic at the intersection of artificial intelligence, data mining, and information security. In this paper, we evaluate the performance of English-trained algorithms on English translations of Romanian-sourced news articles, using a translation-mediated cross-domain evaluation design. The study is based on source code generated with the assistance of artificial intelligence systems for a set of machine learning and transformer-based models. The code was subsequently implemented in Google Colab. 2026, trained on international benchmark datasets, and tested on Romanian news content. This design allowed the rapid prototyping of multiple detection pipelines and the systematic observation of their behavior in a media environment different from that represented in the training corpora. The models were evaluated comparatively using standard classification metrics, including accuracy, precision, recall, and F1-score, complemented by additional indicators relevant to model robustness and practical usability. The experimental results revealed significant differences in performance across algorithms when applied to English translations of Romanian-language news content after training on international datasets. However, this study does not provide a direct comparison between model performance on the international benchmark datasets and the Romanian test corpus; therefore, the gap between the international training corpus and the Romanian-sourced test corpus is interpreted as an exploratory limitation and as a direction for future research. Based on these findings, we propose an empirical classification of the tested models according to their predictive effectiveness, their contextual robustness across linguistic environments, and their operational relevance as filtering tools for institutional monitoring. The results show that AI-assisted coding workflows can provide a viable starting point for reproducible misinformation research, but they also underline the limitations of directly transferring models trained on non-Romanian data to local media ecosystems. The study offers both a replicable evaluation framework and practical insights for institutions involved in strategic communication, public security, and the monitoring of information threats. Full article

Robust multimodal sentiment analysis requires models that can integrate linguistic, acoustic, and visual cues while avoiding over-reliance on noisy nonverbal signals. This study proposes UCMR-Net, a text-anchored residual fusion framework for continuous multimodal sentiment intensity prediction. The model uses contextual textual representations as the primary semantic backbone and introduces acoustic and visual representations as adaptive residual correction signals. Instead of treating the learned positive residual coefficient as a direct estimate of aleatoric or epistemic uncertainty, the proposed framework interprets it as a residual reliability score for regulating nonverbal contribution. Under a unified five-seed evaluation protocol on CMU-MOSI, UCMR-Net achieves MAE = 0.699 ± 0.009, RMSE = 0.997 ± 0.011, Pearson correlation = 0.802 ± 0.006, Acc-2 = 85.82 ± 0.62%, and F1 = 85.60 ± 0.62% (mean ± SD). Controlled ablation results show that text anchoring is the dominant contributor to regression improvement, while residual fusion, adaptive residual weighting, counterfactual distillation, and multi-task supervision provide secondary stabilization effects. Unified missing-modality evaluation further indicates that performance remains relatively stable when audio or visual streams are removed, but degrades substantially when text is unavailable, confirming that the model is text-anchored rather than modality-symmetric. Calibration analysis shows that raw UCMR-Net only modestly improves calibration-related metrics, whereas post-hoc temperature scaling reduces ECE from 0.121 to 0.064 and NLL from 2.337 to 2.286. Additional CMU-MOSEI validation suggests that the proposed fusion strategy generalizes beyond CMU-MOSI, although cross-dataset transfer remains more challenging. Overall, UCMR-Net provides an effective and empirically validated framework for complete-modality sentiment intensity prediction, with moderate robustness under nonverbal missing or corrupted conditions. Full article

Large language models have made fluent fake reviews cheap to produce, eroding the reliability of the linguistic surface that e-commerce platforms have long used to police authenticity. We propose to detect inauthentic reviews not from text in isolation but from their consistency across the multiple relational contexts a review inhabits: its product, its rating, its writing-effort window, and its semantic neighborhood. We model these contexts as a multi-relational hypergraph and equip it with a cellular-sheaf operator whose per-relation consensus residuals quantify how far a review departs from the local agreement of its peers. The resulting Multi-Relational Sheaf-Hypergraph Network (MR-SHN) produces an interpretable, per-relation authenticity profile suitable for trust-and-safety review. On a unified corpus of 46,154 reviews spanning three domains and four authorship conditions, in-distribution detection is essentially saturated (F1 up to 0.980), yet all detectors collapse under domain shift, with $AUC$ falling to 0.48. We document a marked asymmetry: detectors trained on human-written deception transfer to GPT-4-Turbo reviews ($AUC0.88$), while the reverse fails. A training-free residual score separates authentic from modern AI reviews at $AUC0.989$, and the model transfers to six unseen generator families at $AUC0.83$. We report robustness limits candidly and discuss implications for platform governance. Full article

Social media platforms, online reviews, app store feedback, and complaint records are important sources of user-generated textual data for understanding passenger experience in public transport and related mobility contexts. This paper reviews how social media data mining and natural language processing have been used to transform such data into evidence on service quality and satisfaction. The review focuses on public transport while selectively drawing on adjacent mobility domains, including aviation, ride-hailing, micromobility, and app-based mobility, which offer transferable methodological insights. Public transport studies are treated as the core empirical evidence base, whereas adjacent mobility- and methods-oriented studies support conceptual and methodological interpretation rather than direct claims about public transport. The review examines how textual sources are converted into analytical outputs, including sentiment, topics, aspects, emotions, and composite indicators. It shows that sentiment should not be treated as a direct measure of satisfaction but as an intermediate signal of passengers’ expressed perceptions. Key risks include sampling bias, negativity bias, linguistic ambiguity, class imbalance, weak spatial and temporal attribution, platform effects, and limited validation. The paper proposes a validation-oriented framework that maps text-derived signals to service aspects, anchors them in spatial–temporal context, triangulates them with survey, complaint, and operational evidence, and translates them into service-quality-relevant decision-support outputs. Full article

Legal transplants have consolidated as a core issue of contemporary comparative law, although their study remains marked by significant theoretical and methodological tensions. This article presents a PRISMA-informed structured systematic review of 25 studies published between 2008 and 2025 and retrieved through SciSpace, Google Scholar, PubMed, and Web of Science-supported searching, with the aim of identifying conceptual frameworks, recurrent conditions of validity, and practical limitations in recent legal-transplant scholarship. The search yielded 960 exported records; after deduplication and screening, 30 articles were assessed in full text, of which 25 were available and included. The revised article identifies the 25 studies individually and links the descriptive claims to a study-by-study coding table. The findings show five recurrent theoretical lenses: positivist transfer models, culturalist critiques, diffusion mechanisms and multicausal models, communicative metaphors, and mixed transplant concepts. The corpus covers studies in Europe, China, Asia-Pacific, Vietnam, Japan, India, Afghanistan, Pakistan, Hungary, Brazil, Africa, Latin America, and transnational settings. The evidence does not support universal causal claims, but it consistently suggests that contextual compatibility, institutional capacity, local legitimacy, and interpretive adaptation shape the effectiveness of legal transplants, while linguistic barriers, interpretive mismatches, coercive imposition, and weak implementation capacity constrain them. The corpus remains concentrated in corporate law, intellectual property, constitutional law, criminal law, and drug policy, with a predominance of comparative, doctrinal, case-study, and conceptual methodologies. Full article

Virtual influencers are increasingly used in influencer marketing, yet it remains unclear whether trust generated by an artificial persona can be transferred to endorsed brands in the same way as trust generated by human influencers. This study examines artificial intelligence (AI)-mediated trust transfer among female consumers by comparing virtual and human influencers across four social media platforms. Drawing on source credibility, parasocial interaction, social presence, trust transfer, and brand attachment perspectives, we propose that influencer type is associated with brand outcomes through three observable social-cue pathways: perceived authenticity, parasocial interaction, and social presence. These cues are expected to be associated with influencer trust, which is then associated with brand trust, brand attachment, purchase intention, and recommendation intention. Using 78,432 female consumer comments from Xiaohongshu, Instagram, Weibo, and Douyin, matched across 12 virtual–human-influencer pairs, we construct text-derived linguistic indicators—proxies rather than validated psychometric constructs—through keyword dictionaries, sentiment classification, and standardized composite scoring. The results show that human influencers are associated with higher perceived authenticity, parasocial interaction, and social presence than virtual influencers. The strongest association in the model is the trust-transfer path from influencer trust to brand trust, and the indirect path is associated with a substantial share of the observed covariation between influencer type and brand attachment. Product type further qualifies these patterns: the human-influencer advantage is stronger for hedonic products than for utilitarian products. These findings suggest that virtual influencers should not be understood as universal substitutes for human influencers. Instead, their effectiveness depends on whether AI-mediated personas can generate the social and authenticity cues that the literature associates with trust transfer in a given product context. Full article

Spanish–English bilingual learners in U.S. dual language and bilingual education programs develop Spanish orthographic competence while receiving literacy instruction across two writing systems that differ in phonological transparency, orthographic depth, and grammatical marking. This study examined experts’ perceptions of the clarity, instructional coherence, pedagogical relevance, applicability, and refinement priorities of a pedagogical framework for Spanish orthographic development in contexts where Spanish is used as a language of instruction and literacy. The framework conceptualizes Spanish orthographic decision-making as the coordinated activation of phonological mapping, orthographic–grammatical reasoning, and visual–lexical retrieval within biliteracy development. Using a qualitative evaluative design, the study analyzed open-ended questionnaire and interview data from 44 experts in bilingual education and Spanish literacy-related fields. Findings show broad convergence regarding the framework’s clarity, instructional coherence, and relevance for bilingual contexts. Participants emphasized pre-dictation preparation, explicit metalinguistic analysis, visual-memory activation and retrieval routines, and cross-linguistic comparison between Spanish and English. They also identified refinement priorities, including classroom-ready examples, clearer articulation of error and autocorrection, and stronger integration with reading, writing, and oracy practices. This study positions Spanish orthographic instruction as a cognitively guided biliteracy practice and identifies design principles for strengthening orthographic, metalinguistic, and cross-linguistic instruction in bilingual programs. Full article

Recent advances in multi-modal large language models have enabled systems to jointly process text, images, and audio. However, these developments have primarily benefited high-resource languages, leaving many low-resource communities underserved. In response, we introduce Qolda-AVL, a compact five-billion-parameter audio–vision–language model tailored for Kazakh. Qolda-AVL extends our previous Qolda vision–language model by adding a dedicated audio perception branch while maintaining strong visual and linguistic performance. Built on the Qwen3-VL-Thinking backbone, we incorporate Audio DeepStack, which transfers features from three intermediate Whisper encoder layers into the first three layers of the language model using dedicated projections and residual connections. The model is trained through a four-stage pipeline: adapting the Whisper encoder and language model to Kazakh, aligning the new audio branch to the language backbone, and jointly fine-tuning all modules on chain-of-thought reasoning tasks across audio, image, and text. All audio, vision, and language capabilities are evaluated using the model’s native reasoning mode, and a chain-of-thought trace is generated before each final answer during the performance assessment. To facilitate further research, we open-source the model along with the adapted Kazakh versions of four audio benchmarks, covering spoken attribute reasoning, spoken mathematical question answering, and audio captioning with question answering. Full article

This study evaluates zero-shot domain transfer for multilingual sentiment analysis in sustainable urban mobility using XLM-RoBERTa, a transformer pre-trained on social media data and applied to transport reviews without task- or domain-specific fine-tuning. Starting from a manually annotated English corpus of 375 transport-related user reviews, we created sentence-aligned translations in Spanish, French, German, and Italian, yielding a multilingual evaluation dataset of 1875 instances. Results show that the model assigns consistently high confidence to polarized content (mean: 0.76–0.85) and lower confidence to neutral or ambiguous expressions (0.58–0.65), with visible but preliminary cross-lingual variations that require further linguistic validation. Confidence scores are treated as diagnostic indicators of model certainty, not as evidence of correctness or calibration. A qualitative analysis of 113 categorized low-confidence predictions identifies six recurring linguistic patterns associated with model uncertainty (led by translation drift, mixed sentiment, and idiomatic expressions) with substantial inter-annotator agreement (κ = 0.664). By releasing the annotated multilingual dataset and code publicly, this work provides a reproducible exploratory evaluation framework for annotation-scarce, domain-specific multilingual NLP. Full article

Large Language Models (LLMs) are typically safety-aligned using high-resource language data, but it remains unclear whether these constraints transfer reliably across distinct linguistic manifolds. This study examines the mathematical foundations of cross-lingual guardrail degradation using Bengali as a low-resource test case. We evaluate Meta-Llama-3-8B, Gemma-2-9B, and Llama-Guard-3 through an automated English-to-Bengali translation pipeline, paired statistical testing, latent-space visualization, and tokenization-based structural analysis. The results show a statistically significant increase in Gemma-2’s Attack Success Rate from 32.0% in English to 41.2% in Bengali ($p<0.0001$, McNemar’s test), while Llama-Guard-3 fails to detect 39.5% of malicious Bengali prompts. Latent-space projections indicate weaker separation between safe and unsafe Bengali representations, and tokenization analysis shows a 4.69-fold token fertility expansion associated with a normalized perplexity of 887.32. Furthermore, projecting low-resource inputs back into the high-resource latent space successfully restores optimization constraints, whereas natively translating safety prompts exacerbates vulnerability. Together, these findings suggest that cross-lingual safety failures are associated with representational entanglement and token fragmentation rather than only superficial prompt translation effects. The study supports the need for multilingual alignment methods that better account for tokenization geometry, latent-space structure, and language-dependent safety evaluation. Full article

L1 transfer is well-attested in SLA; negative transfer is common when learners encounter a typologically distinct language. English-speaking learners often struggle with Japanese passives, which differ significantly from English passives both conceptually and grammatically. While English passives primarily defocus the agent, Japanese passives serve multiple semantic and discourse functions, often maintaining a focus on (and empathy toward) the experiencer. This small study examines how conceptual understandings drawn from usage-based (UB) analyses influence the acquisition of Japanese passives. Using corpus studies and acquisition research as a foundation, we developed concept-based language instruction (C-BLI) integrating UB-focused concepts. Our analysis of students’ oral languaging, gesture, and story-writing data from an immediate post-test and two delayed (3 weeks and 6 months post-instruction) post-tests show individual differences and demonstrate how a UB-based C-BLI approach facilitated developmental processes in Japanese over time; students improved their grasp of concepts taught via multi-modal materials, including visual materializations of concepts and ocean wave gestures. Conceptual and linguistic development were evidenced via oral languaging and story-writing. The most frequently used passive verb was iu ‘say,’ which has been found to be often passivized in L1 speakers’ production and previous SLA research. Findings contribute to broader discussions of how conceptual restructuring may affect L2 acquisition of complex grammatical constructions. Full article

This study presents a comprehensive review of more than 100 research papers on sign language recognition (SLR) published between 2020 and 2026. The analysis focuses on deep learning approaches applied to video-based SLR, including spatiotemporal feature extraction, temporal modeling, attention mechanisms, motion-based representations, hybrid frameworks, transfer learning methods and other methods. Particular attention is given to how these methods model spatiotemporal dynamics and capture subtle gesture characteristics in sign language communication. The review highlights several recent developments, such as the introduction of specialized datasets, the emergence of real-time recognition systems, and the integration of multimodal fusion strategies. At the same time, persistent challenges remain, including data scarcity in low-resource sign languages, limited linguistic standardization of datasets, and insufficient model interpretability. The findings underline the importance of developing scalable and generalizable models capable of handling diverse datasets and user variability. The distinct contributions of this review are fourfold: (1) a comprehensive synthesis of over 100 studies published between 2020 and 2026, covering the full spectrum of deep learning architectures for video-based SLR; (2) a structured six-category taxonomy enabling systematic cross-architectural comparison; (3) a comprehensive focus on low-resource sign languages, which remain underrepresented in the existing literature; and (4) a critical analysis of the current benchmark landscape for low-resource sign languages, identifying key gaps and outlining strategic directions for future dataset development. These contributions are intended to guide further research toward more robust, inclusive, and universally applicable SLR systems. Full article

Modeling historically situated gender ideology remains challenging for language models, as contemporary embeddings struggle to reflect temporally specific semantic structures beyond surface lexical patterns. Although large language models exhibit extensive general-purpose performance, their direct use with history-specific semantic analysis is limited by the distributional mismatch between contemporary training data and historical linguistic patterns. These constraints encourage the distillation of temporally based semantic knowledge into small student architectures. To solve this issue, we propose Temporally Informed Distillation of Embedding Semantics (TIDES), which integrates continued pretraining on temporally specific corpora with feature-level distillation from large embedding teachers. We evaluate TIDES across teacher architectures with distinct pretraining objectives. While continued pretraining provides lexical and syntactic adaptation, our results show that improvements in ideological modeling cannot be attributed to additional training exposure alone. Rather, teacher–student structural alignment is also critical to transfer effectiveness. Contrastive, encoder-aligned teachers yield substantially more stable preservation of fine-grained, historically situated semantic distinctions. These findings suggest that temporal ideology transfer is representation-dependent: ideological meaning can be shaped by the geometry and training objectives of embedding spaces. By introducing TIDES and providing evidence that architectural compatibility can influence ideological inheritance, this study advances a representation-centered account of modeling ideology in temporally grounded semantic research. Full article