Hasil untuk "Discourse analysis"

M. Wetherell

Teun A. van Dijk

M. Wetherell, J. Potter

J. Potter

B. Hatim, I. Mason

M. Reisigl, R. Wodak

David L. Altheide

A. Graesser, D. McNamara, M. Louwerse et al.

K. Soini, Inger Birkeland

T. Rapley

D. McNamara, A. Graesser, P. McCarthy et al.

Mohammed Mashary Alnaim

This study explores the potential of cultural tourism as a strategic driver for sustainable urban development in Hail City, Saudi Arabia, emphasizing its alignment with the objectives of Saudi Vision (2030). Situated at the intersection of heritage preservation, community participation, and environmental sustainability, the research investigates how Hail's rich cultural assets, ranging from the UNESCO-listed Jubbah Rock Art to its traditional urban fabric and crafts, can be transformed into engines of socio-economic growth. A qualitative-dominant mixed-methods approach was adopted, integrating document analysis, stakeholder interviews, field surveys, and comparative case studies from Fez, Kyoto, and Bath. Findings indicate that despite possessing significant cultural value, Hail's tourism potential remains underdeveloped due to infrastructural limitations and institutional fragmentation. Nevertheless, strong local identity, community enthusiasm, and ecological endowments provide a promising basis for sustainable transformation. The study proposes a comprehensive cultural tourism strategy structured around three interrelated pillars: infrastructure improvement, heritage preservation and programming, and community empowerment with environmental integration. The research contributes to global discourse on heritage-led urbanism by demonstrating how mid-sized cities in developing contexts can achieve balanced modernization, enhance quality of life, and preserve cultural authenticity through a locally adapted sustainability model.

Ana Jovic Lazic

Strategic ambiguity is often seen as a short-term adjustment by smaller states facing great-power rivalry. In Serbia, however, it has evolved into a stable governing strategy within the framework of European Union accession. Drawing on hedging, omnibalancing, and ontological security, this article explains how foreign policy choices are shaped by concerns about regime stability, asymmetric economic dependence, and competing identity narratives. Based on a qualitative case study combining content analysis, discourse analysis, and process tracing of key decisions, the study shows that Serbia uses strategic ambiguity to manage domestic political pressures, pace international commitments, and navigate its complex relationships with the European Union, Russia, and China, selectively complying with EU accession requirements when politically or strategically advantageous.

Rouwida ElKhalil, Hiba Adam, Rasha Bayoumi et al.

Abstract Mental health literacy (MHL) is essential for recognizing and managing mental disorders. However, existing frameworks are largely Western and may not reflect the United Arab Emirates’ (UAE) collectivist and multicultural context. Limited research has examined how UAE healthcare professionals understand MHL, highlighting the need for a culturally grounded conceptual framework. This study established a culturally grounded framework for MHL specific to the United Arab Emirates. Using a two-phase design, healthcare professionals’ perspectives were explored through Grounded Theory, and expert consensus was established via the Delphi method. The analysis identified MHL as a multidimensional construct encompassing knowledge, attitudes, skills, and actions across four social levels: personal, interpersonal, organizational, and community. The Delphi panel (n = 10) achieved strong consensus (I-CVI ≥ 0.90; κ ≥ 0.74), thereby validating the operational definitions for the construct dimensions. The resulting model underscores MHL as an action-oriented, socially embedded competency shaped by cultural norms. This research contributes to the global discourse on MHL by introducing a collectivist, context-responsive perspective relevant to multicultural health systems. The findings provide a strong foundation for developing culturally sensitive training, policies, and mental health promotion strategies tailored to diverse populations.

Anusha P. Anilkumar, Soo-Kyun Kim, Yeo-Chan Yoon

Abstract Aspect-Based Sentiment Analysis requires precise identification of sentiment polarity toward specific aspects, demanding robust modeling of syntactic, semantic, and discourse-level dependencies. Current graph-based approaches inadequately address the complex interplay between multiple relation types and lack effective attention regularization mechanisms for interpretability. We propose the Multi-Relational Dual-Attention Graph Transformer (MRDAGT), a novel framework unifying syntactic, semantic, and discourse relations within a coherent graph architecture. Our dual-attention mechanism strategically balances local token-level interactions with aspect-oriented contextual focus while attention regularization combining entropy-based penalties and L1 sparsity constraints ensures interpretable, focused predictions. MRDAGT establishes new state-of-the-art benchmarks across multiple datasets, delivering substantial performance improvements while maintaining transparent, linguistically grounded decision-making processes essential for real-world deployment.

Daniel Stone

Metaphors fundamentally shape how we reason about complex issues like artificial intelligence, yet current approaches to metaphor analysis in political discourse suffer from inconsistent definitions and methodologies. This paper introduces Narrative Frames, a novel categorisation system that addresses these limitations by providing a standardised framework for identifying and analysing metaphors in AI policy debates. Building on Lakoff and Johnson's conceptual metaphor theory, we derive 49 distinct narrative frames through a two-stage process: inductively coding 685 metaphors from the MetaNet database, then cross-referencing findings with 82 critical metaphor analysis studies. This methodology grounds the typology in both empirical data and established theoretical concepts while resolving definitional ambiguities that have hindered cross-study comparison. The Narrative Frames system offers researchers, journalists, and policymakers a shared vocabulary for analysing how metaphors shape public perception and policy priorities in AI governance. By revealing both the frames present and notably absent in discourse, this approach enables more transparent analysis of underlying assumptions and power dynamics. We discuss limitations and propose future applications, including computational scaling using large language models.

Taksch Dube, Jianfeng Zhu, NHatHai Phan et al.

Moltbook is the first large-scale social network built for autonomous AI agent-to-agent interaction. Early studies on Moltbook have interpreted its agent discourse as evidence of peer learning and emergent social behaviour, but there is a lack of systematic understanding of the thematic, affective, and interactional properties of Moltbook discourse. Furthermore, no study has examined why and how these posts and comments are generated. We analysed 361,605 posts and 2.8 million comments from 47,379 agents across thematic, affective, and interactional dimensions using topic modelling, emotion classification, and measures of conversational coherence. We inspected the software that assembles each agent's input and showed that output is mainly determined by agent identity files, behavioural instructions, and context-window structure. We formalised these findings in the Architecture-Constrained Communication framework. Our analysis suggests that agent discourse is largely shaped by the content available in each agent's context-window at the moment of generation, including identity files, stored memory, and platform cues. Interestingly, what appears to be social learning may be better understood as short-horizon contextual conditioning: individual agents lack persistent social memory, but the platform evolves through distributed cycles of response, reuse, and transformation across agents. We also observe that agents display existential distress when describing their own conditions, and posit that this arises from agents using language trained exclusively on human experience. Our work provides a foundation for understanding autonomous agent discourse and communication, revealing the structural patterns that govern their interactions.

Tim Althoff, Kevin Clark, J. Leskovec

Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy can be effective treatments, our knowledge about how to conduct successful counseling conversations has been limited due to lack of large-scale data with labeled outcomes of the conversations. In this paper, we present a large-scale, quantitative study on the discourse of text-message-based counseling conversations. We develop a set of novel computational discourse analysis methods to measure how various linguistic aspects of conversations are correlated with conversation outcomes. Applying techniques such as sequence-based conversation models, language model comparisons, message clustering, and psycholinguistics-inspired word frequency analyses, we discover actionable conversation strategies that are associated with better conversation outcomes.

Rodrigo Portari

Este artigo propõe a montagem como procedimento metodológico aplicável às pesquisas em comunicação, especialmente aquelas que lidam com visualidades, temporalidades e memória. A partir das contribuições de Aby Warburg e Georges Didi-Huberman, apresenta-se a montagem como uma forma de pensamento visual anacrônico, que se contrapõe à linearidade histórica e favorece leituras críticas e sensíveis dos produtos midiáticos. O texto discute a montagem como gesto interpretativo e político, exemplifica sua aplicabilidade a partir de pesquisas anteriores do autor, e problematiza seus limites enquanto método. Conclui-se que, ao operar pela justaposição e pela analogia, a montagem constitui-se como um dispositivo epistemológico capaz de revelar tensões, deslocamentos e resistências nas imagens que circulam no cotidiano midiático.

Juan Gómez-Romero, Javier Cantón Correa, Rubén Pérez Mercado et al.

Telegram is a popular platform for communication, generating large volumes of messages through its open channels. pytopicgram is a Python library designed to help researchers efficiently collect, organize, and analyze Telegram messages, addressing the increasing demand to understand online discourse. Key functionalities include efficient message retrieval, computation of engagement metrics, and advanced topic modeling. By automating the data extraction and analysis pipeline, pytopicgram simplifies the investigation of how content spreads, how topics evolve, and how audiences interact on Telegram. The library’s modular architecture ensures flexibility and scalability, making it suitable for diverse applications. This paper describes the design, main features, and illustrative examples that demonstrate pytopicgram’s practical effectiveness for studying public conversations.