Hasil untuk "Discourse analysis"

T. V. Dijk

R. Ingold

Yining Wang, Alhasan Abdellatif, Artemis Deligianni et al.

We present the first large-scale comparative analysis of Truth Social and the most popular conservative Reddit communities, r/Conservative, r/conservatives, and r/Republican. Using topic modeling with FASTopic and LLM-assisted refinement, we analyze topic prevalence, toxicity, and temporal dynamics across these communities during the first eight months of Truth Social. We find clear contrasts: Truth Social centers on grievance and narrative-driven content, while Reddit focuses more on policy debates. Toxicity is higher on Reddit and peaks in cultural and leader-focused topics. Despite similar event-driven participation shocks across platforms, Truth Social shows higher baseline proportions of users engaging with political topics. Our findings contribute to understanding how alternative right-leaning platforms reshape online discourse.

Samantha Sudhoff, Pranav Perumal, Zhaoqing Wu et al.

Climate discourse online plays a crucial role in shaping public understanding of climate change and influencing political and policy outcomes. However, climate communication unfolds across structurally distinct platforms with fundamentally different incentive structures: paid advertising ecosystems incentivize targeted, strategic persuasion, while public social media platforms host largely organic, user-driven discourse. Existing computational studies typically analyze these environments in isolation, limiting our ability to distinguish institutional messaging from public expression. In this work, we present a comparative analysis of climate discourse across paid advertisements on Meta (previously known as Facebook) and public posts on Bluesky from July 2024 to September 2025. We introduce an interpretable, end-to-end thematic discovery and assignment framework that clusters texts by semantic similarity and leverages large language models (LLMs) to generate concise, human-interpretable theme labels. We evaluate the quality of the induced themes against traditional topic modeling baselines using both human judgments and an LLM-based evaluator, and further validate their semantic coherence through downstream stance prediction and theme-guided retrieval tasks. Applying the resulting themes, we characterize systematic differences between paid climate messaging and public climate discourse and examine how thematic prevalence shifts around major political events. Our findings show that platform-level incentives are reflected in the thematic structure, stance alignment, and temporal responsiveness of climate narratives. While our empirical analysis focuses on climate communication, the proposed framework is designed to support comparative narrative analysis across heterogeneous communication environments.

Maria Lipińska, Nina Kotula, Dariusz Jemielniak

Abstract This study investigates expert figures’ roles in alien-related UFO conspiracy theories, focusing on their impact on public perception through social media analysis. Utilizing a blend of content and trend analysis, we examine the invocation of scientific authority in UFO conspiracy narratives, identifying a reliance on expert endorsement to legitimize claims about extraterrestrial activity and government secrecy. Findings highlight a common use of expert figures, often without empirical backing, to bolster conspiracy theories.The research reveals the challenge of distinguishing credible information from conspiracy in a landscape where expert authority is easily co-opted. This underscores the importance of scientific literacy and critical thinking in combating disinformation. The study’s implications extend to educational and policy measures aimed at fostering a skeptical and informed public debate on controversial topics. By exploring the dynamics between authority, belief, and disinformation, this work contributes to understanding the mechanisms behind the spread of conspiracy theories and the complex role of expertise in shaping public discourse in the digital age.

Jaya Singh Parihar, Dariusz Cichon, Aeshna Kharbanda et al.

Objective: People often glorify the tourism industry as a gateway for entrepreneurial opportunities, particularly for women. However, its structural inequalities remain largely unaddressed. While women play a significant role in tourism entrepreneurship, systemic barriers constrain their participation, including limited access to capital, restrictive social norms, and gendered labour segmentation. Existing research primarily frames empowerment in financial terms, neglecting broader socio-cultural and structural constraints. Feminist critiques of tourism entrepreneurship remain marginal, creating gaps in academic inquiry, and policy interventions. Research Design & Methods: We conducted an integrative literature review, systematically examining research on gender, tourism, and entrepreneurship through a dual analytic approach incorporating inductive and deductive coding (Donthu et al., 2021). Using the PRISMA framework, we synthesised high-impact literature to identify thematic patterns, research gaps, and structural constraints affecting women entrepreneurs in tourism. Findings: Findings reveal persistent gender disparities, particularly in financial accessibility, decision-making power, and policy design. The thematic analysis highlights key dimensions such as gender inclusivity, sustainable tourism, and feminist entrepreneurship, advocating for an intersectional approach that incorporates gender-sensitive frameworks into tourism research (Cobo et al., 2015). Implications & Recommendations: Policy recommendations emerging from this study include targeted funding mechanisms, comprehensive training and mentorship programs, and regulatory reforms designed to support women-led enterprises in sustainable tourism. By integrating feminist perspectives into tourism entrepreneurship discourse, this research advances theoretical understanding while offering actionable recommendations for policymakers. Addressing gendered barriers in tourism entrepreneurship is critical for achieving sustainable development goals and fostering inclusive economic growth. Contribution & Value Added: This study’s originality lies in its integrative application of feminist perspectives to critically analyse gendered barriers in tourism entrepreneurship. By employing a dual analytic approach and emphasising intersectional insights, it offers a nuanced understanding of structural inequalities and actionable policy recommendations for sustainable and inclusive tourism development.

Fakron Jamalin

A contextual Arabic textbook was compiled as a religious moderation program in UIN Maulana Malik Ibrahim, Indonesia. The moderation program was initiated by the Ministry of Religious Affairs. A textbook showing Indonesian culture was developed to differentiate Arabic textbooks from other countries, such as Germany and Saudi Arabia. The textbook contains four distinct indicators.  This study explores the concordance and collocation of four indicators of religious moderation in Arabiyah Lil Hayah (ALH). It also investigated students’ perceptions of the four indicators. Employing corpus-based critical discourse analysis, this study collected a corpus from the ALH book on student perceptions in txt form. This study identified that the indicator of National Commitment is present in several contexts, such as August 17, Islamic organizations, and elections. The Tolerance indicator is represented by the different opinions of the four madzhabs and the motto of Bhinneka Tunggal Ika. The teaching of non-violence was described in the Hajar Aswad incident in Makkah. Local culture was introduced in the context of culture, ethnicity, and traditional food. Students use a variety of sentences to express their perceptions, including cause-and-effect, supposition, and rhetorical forms. When the discourse is personally relevant, pronouns are used to reflect this connection, with their expression of identity often aligned with their social community or gender, such as affiliations with NU (Nahdlatul Ulama) or their identity as women.

William Sheffield, Kanishka Misra, Valentina Pyatkin et al.

Discourse particles are crucial elements that subtly shape the meaning of text. These words, often polyfunctional, give rise to nuanced and often quite disparate semantic/discourse effects, as exemplified by the diverse uses of the particle "just" (e.g., exclusive, temporal, emphatic). This work investigates the capacity of LLMs to distinguish the fine-grained senses of English "just", a well-studied example in formal semantics, using data meticulously created and labeled by expert linguists. Our findings reveal that while LLMs exhibit some ability to differentiate between broader categories, they struggle to fully capture more subtle nuances, highlighting a gap in their understanding of discourse particles.

Viet-Thanh Pham, Minghan Wang, Hao-Han Liao et al.

Adapting large language models to full document translation remains challenging due to the difficulty of capturing long-range dependencies and preserving discourse coherence throughout extended texts. While recent agentic machine translation systems mitigate context window constraints through multi-agent orchestration and persistent memory, they require substantial computational resources and are sensitive to memory retrieval strategies. We introduce TransGraph, a discourse-guided framework that explicitly models inter-chunk relationships through structured discourse graphs and selectively conditions each translation segment on relevant graph neighbourhoods rather than relying on sequential or exhaustive context. Across three document-level MT benchmarks spanning six languages and diverse domains, TransGraph consistently surpasses strong baselines in translation quality and terminology consistency while incurring significantly lower token overhead.

The VEXAG Exploration Strategy Study Analysis Workgroup

The 2023-2032 Planetary Science and Astrobiology Decadal Survey Origins, Worlds, and Life recommended that "NASA develop scientific exploration strategies, as it has for Mars, in areas of broad scientific importance, e.g., Venus... that have an increasing number of U.S. missions and international collaboration opportunities" (OWL, p.22-10). In NASA's initial responses to that Decadal Survey, the agency asserted that "...specific scientific exploration strategies should be community generated by bodies such as the Analysis Groups," thus placing the onus on the planetary community to generate and support these exploration strategies. In late 2022, the Venus Exploration Analysis Group began a project to develop a new exploration strategy for Venus, reflecting the 2021 selections of the VERITAS, DAVINCI, and EnVision missions and the sweeping comparative planetology recommendations relevant to Venus in Origins, Worlds, and Life. This is that strategy. Taking a broad look at the scientific, technological, and programmatic advances required to address the key outstanding questions that Venus poses, and predicated on VERITAS, DAVINCI, and EnVision flying as planned in the early 2030s, this report outlines a set of actions available to NASA, VEXAG, and the planetary science community at large to establish a sustained program of Venus exploration in the years and decades ahead. Key to this approach is recognizing Venus as a unique setting where multiple, cross-disciplinary, Decadal-level planetary, Earth, heliophysics, and exoplanet science questions can be addressed, as well as being a worthy target of exploration in its own right. This report offers Assessments of the current state of Venus exploration, and Actions for the U.S. and international Venus community, as well as NASA, to consider. This strategy is a living document and should be updated as warranted.

Minas Gadalla, Sotiris Nikoletseas, José Roberto de A. Amazonas

This research explores the interdisciplinary interaction between psychoanalysis and computer science, suggesting a mutually beneficial exchange. Indeed, psychoanalytic concepts can enrich technological applications involving unconscious, elusive aspects of the human factor, such as social media and other interactive digital platforms. Conversely, computer science, especially Artificial Intelligence (AI), can contribute quantitative concepts and methods to psychoanalysis, identifying patterns and emotional cues in human expression. In particular, this research aims to apply computer science methods to establish fundamental relationships between emotions and Lacanian discourses. Such relations are discovered in our approach via empirical investigation and statistical analysis, and are eventually validated in a theoretical (psychoanalytic) way. It is worth noting that, although emotions have been sporadically studied in Lacanian theory, to the best of our knowledge a systematic, detailed investigation of their role is missing. Such fine-grained understanding of the role of emotions can also make the identification of Lacanian discourses more effective and easy in practise. In particular, our methods indicate the emotions with highest differentiation power in terms of corresponding discourses; conversely, we identify for each discourse the most characteristic emotions it admits. As a matter of fact, we develop a method which we call Lacanian Discourse Discovery (LDD), that simplifies (via systematizing) the identification of Lacanian discourses in texts. Although the main contribution of this paper is inherently theoretical (psychoanalytic), it can also facilitate major practical applications in the realm of interactive digital systems. Indeed, our approach can be automated through Artificial Intelligence methods that effectively identify emotions (and corresponding discourses) in texts.

T. V. Dijk

Oleksandra Povietkina

This article is devoted to the study of the polysemy of the verb predicate верить in the cognitive-discursive methodology. The relevance of the research is due to the irrelevance of the traditional approach, the impossibility of describing the mechanisms of the development of word meanings as metaphorical, metonymic, or functional transference, as well as the need to understand and explain the contradictions in the semantics of the verbal predicate that arise in the contexts of various discourses. As a result of the analysis, a system of parameters was determined, which constitutes a single analytical apparatus of the cognitive-discursive methodology. On the basis of the analysis, we explicated cognitive features, which in our opinion, embodies the gestalt script of the religious conceptual space of Faith, determined the internal form of the concept of Faith, which acts as a cognitive script and induces all the contextual meanings of the verb predicate верить; compiled typology of the contexts of usage of the verb and differentiated several of its meanings. The subject of our attention was the meaningful difference between the contexts of religious discourse (я верю в бога) and common (я верю в тебя/ прогресс компьютерной техники). The use of terms gestalt, background-figure, focusing-defocusing made it possible to compare the semantics of the verbal predicate in the contexts of religious and common discourses, as well as to trace the distribution of attention in the semantic-syntactic organization of different contexts and the game of cognitive focus at the conceptual level (gestalt scenario). Focusing the interlocutor's attention on the meanings required in the course of religious/common communication, as well as obscuring the background meanings ensures the polysemy of the verb predicate верить.

Katherine M. Engelke

Audience participation is a contested issue in newsrooms and can challenge journalistic authority. By conducting a mixed-method analysis of a decade (2009–2018) of metajournalistic discourse (<i>N</i> = 135) on participatory journalism in two leading trade magazines in the US and Germany (<i>Columbia Journalism Review</i> and <i>Journalist</i>), this study aims to contribute to the field’s understanding of how and in which contexts audience participation is covered in public discourse and of reasons for positive and negative public evaluations of participatory journalism. The results show that while metajournalistic discourse covered participatory journalism in all stages of the news production process, notable differences in the coverage emerged depending on the specific context factors of participation dealt with. It is therefore depicted as a pervasive and multi-faceted phenomenon. 93 articles featured an evaluation: 53% depicted participatory journalism positively, 16% negatively and 31% left a mixed impression. Several themes emerged in the reasons for these evaluations, some of which are exact opposites, indicating that the presented evaluation depends on the specific circumstances of audience participation, namely the contexts of participatory journalism, the degree of involvement and character of audience participation and the resources available to the journalists.

Iraj Soleimanjahan, Hiwa Weisi

This study attempts to examine Tom Wolfe’s The Electric Kool-Aid Acid Test in the light of the ideas of Michel Foucault, specifically the notions of normalization, institutions, and surveillance that tackle the relationships among power, institutions, and literature. The analysis posits that, in the context of the 1960s, the American government took advantage of all the institutions which were supposed to guarantee the freedom of individuals to curtail their freedom. Seeking to create a normal, ordinary, and homogeneous society, these administrations have employed the police, law, prison, and other overlapping institutions that work in tandem to create circuits of institutions which guarantee to reduce  human beings to simpletons who are docile, meek, and ready to fit in place properly. The normality and ordinariness favored by the authorities are also implemented since the novel starts by depicting free individuals whose identities hinge on their being abnormal while it ends when their movement is shattered, and the protagonist is seen as a simpleton serving the forced labor sentence of the judges of both the government and normality. Freedom emerges as a mirage than truth as there seems to be no outside through which individuals can live outside the domination of controlling apparatuses.

Chunkit Chan, Xin Liu, Jiayang Cheng et al.

Implicit Discourse Relation Recognition (IDRR) is a sophisticated and challenging task to recognize the discourse relations between the arguments with the absence of discourse connectives. The sense labels for each discourse relation follow a hierarchical classification scheme in the annotation process (Prasad et al., 2008), forming a hierarchy structure. Most existing works do not well incorporate the hierarchy structure but focus on the syntax features and the prior knowledge of connectives in the manner of pure text classification. We argue that it is more effective to predict the paths inside the hierarchical tree (e.g., "Comparison -> Contrast -> however") rather than flat labels (e.g., Contrast) or connectives (e.g., however). We propose a prompt-based path prediction method to utilize the interactive information and intrinsic senses among the hierarchy in IDRR. This is the first work that injects such structure information into pre-trained language models via prompt tuning, and the performance of our solution shows significant and consistent improvement against competitive baselines.

Ruijun Chen, Jin Wang, Liang-Chih Yu et al.

Maintaining engagement and consistency is particularly important in dialogue systems. Existing works have improved the performance of dialogue systems by intentionally learning interlocutor personas with sophisticated network structures. One issue with this approach is that it requires more personal corpora with annotations. Additionally, these models typically perform the next utterance prediction to generate a response but neglect the discourse coherence in the entire conversation. To address these issues, this study proposes a method of learning to memorize entailment and discourse relations for persona-consistent dialogue tasks. Entailment text pairs in natural language inference dataset were applied to learn latent entailment relations as external memories by premise-to-hypothesis generation task. Furthermore, an internal memory with a similar architecture was applied to the discourse information in the dialogue. Placing orthogonality restrictions on these two memory spaces ensures that the latent entailment relations remain dialogue-independent. Both memories collaborate to obtain entailment and discourse representation for the generation, allowing a deeper understanding of both consistency and coherence. Experiments on two large public datasets, PersonaChat and DSTC7-AVSD, demonstrated the effectiveness of the proposed method. Both automatic and human evaluations indicate that the proposed model outperforms several strong baselines in terms of both persona consistency and response coherence. Our source code is available at https://github.com/Chenrj233/LMEDR.

Chuyuan Li, Patrick Huber, Wen Xiao et al.

Discourse processing suffers from data sparsity, especially for dialogues. As a result, we explore approaches to build discourse structures for dialogues, based on attention matrices from Pre-trained Language Models (PLMs). We investigate multiple tasks for fine-tuning and show that the dialogue-tailored Sentence Ordering task performs best. To locate and exploit discourse information in PLMs, we propose an unsupervised and a semi-supervised method. Our proposals achieve encouraging results on the STAC corpus, with F1 scores of 57.2 and 59.3 for unsupervised and semi-supervised methods, respectively. When restricted to projective trees, our scores improved to 63.3 and 68.1.

Oksana Shadura, Alexander Held

The IRIS-HEP Analysis Grand Challenge (AGC) is designed to be a realistic environment for investigating how analysis methods scale to the demands of the HL-LHC. The analysis task is based on publicly available Open Data and allows for comparing the usability and performance of different approaches and implementations. It includes all relevant workflow aspects from data delivery to statistical inference. The reference implementation for the AGC analysis task is heavily based on tools from the HEP Python ecosystem. It makes use of novel pieces of cyberinfrastructure and modern analysis facilities in order to address the data processing challenges of the HL-LHC. This contribution compares multiple different analysis implementations and studies their performance. Differences between the implementations include the use of multiple data delivery mechanisms and caching setups for the analysis facilities under investigation.

Yiyang Li, Xinting Huang, Wei Bi et al.

Multi-party dialogues are more difficult for models to understand than one-to-one two-party dialogues, since they involve multiple interlocutors, resulting in interweaving reply-to relations and information flows. To step over these obstacles, an effective way is to pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. However, due to the lack of explicitly annotated discourse labels in multi-party dialogue corpora, previous works fail to scale up the pre-training process by putting aside the unlabeled multi-party conversational data for nothing. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model by unsupervised latent variable inference methods. Experiments on multiple downstream tasks show that our pre-trained model outperforms strong baselines by large margins and achieves state-of-the-art (SOTA) results, justifying the effectiveness of our method. The official implementation of this paper is available at https://github.com/EricLee8/MPD_EMVI.