Hasil untuk "Discourse analysis"

J. Blommaert

This engaging 2005 introduction offers a critical approach to discourse, written by an expert uniquely placed to cover the subject for a variety of disciplines. Organised along thematic lines, the book begins with an outline of the basic principles, moving on to examine the methods and theory of CDA (critical discourse analysis). It covers topics such as text and context, language and inequality, choice and determination, history and process, ideology and identity. Blommaert focuses on how language can offer a crucial understanding of wider aspects of power relations, arguing that critical discourse analysis should specifically be an analysis of the 'effects' of power, what power does to people, groups and societies, and how this impact comes about. Clearly argued, this concise introduction will be welcomed by students and researchers in a variety of disciplines involved in the study of discourse, including linguistics, linguistic anthropology and the sociology of language.

Ken Hyland

R. Rogers, Elizabeth Malancharuvil-Berkes, M. Mosley et al.

Ali Baigelenov, Prakash Shukla, Phuong Bui et al.

Data visualization practitioners routinely invoke inspiration, yet we know little about how it is constructed in public conversations. We conduct a discourse analysis of 31 episodes from five popular data visualization podcasts. Podcasts are public-facing and inherently performative: guests manage impressions, articulate values, and model "good practice" for broad audiences. We use this performative setting to examine how legitimacy, identity, and practice are negotiated in community talk. We show that "inspiration talk" is operative rather than ornamental: speakers legitimize what counts, who counts, and how work proceeds. Our analysis surfaces four adjustable evaluation criteria by which inspiration is judged-novelty, authority, authenticity, and affect-and three operative metaphors that license different practices-spark, muscle, and resource bank. We argue that treating inspiration as a boundary object helps explain why these frames coexist across contexts. Findings provide a vocabulary for examining how inspiration is mobilized in visualization practice, with implications for evaluation, pedagogy, and the design of galleries and repositories that surface inspirational examples.

R. Wooffitt

Rahman Sharifzadeh

Reexamining science journalism through the constructivist lens of Science and Technology Studies (STS), the present paper argues that this perspective promotes a more responsible approach to reporting scientific discoveries in medicine. The dominant anti-constructivist, realist approach often results in what we term "dramatic modalization," which attributes greater facticity and universality to scientific findings than they actually possess at the time of publication, leading to significant moral consequences.To illustrate this, we will first explore the STS perspective as a framework for understanding the construction of facts in practice. Next, through a discourse analysis of two historical cases in medical journalism—the MMR-autism link and the depression-serotonin connection—we will demonstrate that the realist media coverage of these cases engaged in dramatic modalization, resulting in tangible moral repercussions. We hereby propose an alternative STS model for science journalism in medicine, arguing that it offers a more morally responsible approach. 

Yang Zhong, Diane Litman

Detecting factual inconsistency for long document summarization remains challenging, given the complex structure of the source article and long summary length. In this work, we study factual inconsistency errors and connect them with a line of discourse analysis. We find that errors are more common in complex sentences and are associated with several discourse features. We propose a framework that decomposes long texts into discourse-inspired chunks and utilizes discourse information to better aggregate sentence-level scores predicted by natural language inference models. Our approach shows improved performance on top of different model baselines over several evaluation benchmarks, covering rich domains of texts, focusing on long document summarization. This underscores the significance of incorporating discourse features in developing models for scoring summaries for long document factual inconsistency.

Rafiu Adekoya Badekale, Adewale Akinfaderin

Understanding how policy language evolves over time is critical for assessing global responses to complex challenges such as climate change. Temporal analysis helps stakeholders, including policymakers and researchers, to evaluate past priorities, identify emerging themes, design governance strategies, and develop mitigation measures. Traditional approaches, such as manual thematic coding, are time-consuming and limited in capturing the complex, interconnected nature of global policy discourse. With the increasing relevance of unsupervised machine learning, these limitations can be addressed, particularly under high-volume, complex, and high-dimensional data conditions. In this work, we explore a novel approach that applies the dynamic embedded topic model (DETM) to analyze the evolution of global climate policy discourse. A probabilistic model designed to capture the temporal dynamics of topics over time. We collected a corpus of United Nations Framework Convention on Climate Change (UNFCCC) policy decisions from 1995 to 2023, excluding 2020 due to the postponement of COP26 as a result of the COVID-19 pandemic. The model reveals shifts from early emphases on greenhouse gases and international conventions to recent focuses on implementation, technical collaboration, capacity building, finance, and global agreements. Section 3 presents the modeling pipeline, including preprocessing, model training, and visualization of temporal word distributions. Our results show that DETM is a scalable and effective tool for analyzing the evolution of global policy discourse. Section 4 discusses the implications of these findings and we concluded with future directions and refinements to extend this approach to other policy domains.

Abhinav Pratap, Amit Pathak

This paper examines how social media algorithms and filter bubbles contribute to the fragmentation of online discourse, fostering ideological divides and undermining shared understanding. Drawing on Michael Sandels philosophical emphasis on community and shared values, the study explores how digital platforms amplify discrimination discourse including sexism, racism, xenophobia, ableism, homophobia, and religious intolerance during periods of heightened societal tension. By analyzing the dynamics of digital communities, the research highlights mechanisms driving the emergence and evolution of discourse fragments in response to real world events. The findings reveal how social media structures exacerbate polarization, restrict cross group dialogue, and erode the collective reasoning essential for a just society. This study situates philosophical perspectives within a computational analysis of social media interactions, offering a nuanced understanding of the challenges posed by fragmented discourse in the digital age.

Zae Myung Kim, Kwang Hee Lee, Preston Zhu et al.

With the advent of large language models (LLM), the line between human-crafted and machine-generated texts has become increasingly blurred. This paper delves into the inquiry of identifying discernible and unique linguistic properties in texts that were written by humans, particularly uncovering the underlying discourse structures of texts beyond their surface structures. Introducing a novel methodology, we leverage hierarchical parse trees and recursive hypergraphs to unveil distinctive discourse patterns in texts produced by both LLMs and humans. Empirical findings demonstrate that, although both LLMs and humans generate distinct discourse patterns influenced by specific domains, human-written texts exhibit more structural variability, reflecting the nuanced nature of human writing in different domains. Notably, incorporating hierarchical discourse features enhances binary classifiers' overall performance in distinguishing between human-written and machine-generated texts, even on out-of-distribution and paraphrased samples. This underscores the significance of incorporating hierarchical discourse features in the analysis of text patterns. The code and dataset are available at https://github.com/minnesotanlp/threads-of-subtlety.

Deborah Lupton

Kason Ka Ching Cheung, Ho-Yin Chan, Sibel Erduran

Abstract News media plays a vital role in communicating scientific evidence to the public during the COVID-19 pandemic. Such communication is important for convincing the public to follow social distancing guidelines and to respond to health campaigns such as vaccination programmes. However, newspapers were criticised that they focus on the socio-political perspective of science, without explaining the nature of scientific works behind the government’s decisions. This paper examines the connections of the nature of science categories in the COVID-19 era by four local newspapers in the United Kingdom between November 2021 to February 2022. Nature of science refers to different aspects of how science works such as aims, values, methods and social institutions of science. Considering the news media may mediate public information and perception of scientific stories, it is relevant to ask how the various British newspapers covered aspects of science during the pandemic. In the period explored, Omicron variant was initially a variant of concern, and an increasing number of scientific evidence showed that the less severity of this variant might move the country from pandemic to endemic. We explored how news articles communicate public health information by addressing how science works during the period when Omicron variants surge. A novel discourse analysis approach, epistemic network analysis is used to characterise the frequency of connections of categories of the nature of science. The connection between political factors and the professional activities of scientists, as well as that with scientific practices are more apparent in left-populated and centralist outlets than in right-populated news outlets. Among four news outlets across the political spectrum, a left-populated newspaper, the Guardian, is not consistent in representing relations of different aspects of the nature of scientific works across different stages of the public health crisis. Inconsistency of addressing aspects of scientific works and a downplay of the cognitive-epistemic nature of scientific works likely lead to failure in trust and consumption of scientific knowledge by the public in the healthcare crisis.

Arthur Felipe Fiel, Alice Santos, Patrícia Cardoso D'Abreu

Este trabalho busca analisar o filme – Tainá:  uma aventura na Amazônia, direção de Tânia Lamarco e Sérgio Bloch, 2000, que possui a primeira representação de uma criança do gênero feminino em papel ativo e proativo diante das nuances e desfechos da trama. A obra é analisada em seu contexto sociocultural e momento histórico e traz à tona as tensões que nesse contexto se apresentam. Por meio do levantamento teórico, apontamos algumas importantes transformações narrativas que reconfiguram o cinema e o audiovisual infantil brasileiro e, em especial, buscamos ressaltar a importante colaboração das pautas feministas para o avanço do campo dos estudos relacionados às crianças e às infâncias, bem como para as obras produzidas e endereçadas ao público infantil no Brasil.

Karina Coelho Pires, Rafaela Araújo Jordão Rigaud Peixoto

There have been new facets of multilateralism, which have motivated the realignment of traditional power relations established globally, especially regarding the United States and China. This new strategic environment can be observed in changes made to the Brazilian National Defense White Paper (LBDN) of 2020, as well as in the dialectic between white papers of the United States (2017), and China (2019). To investigate these realignments and their possible impacts on the Brazilian defense sector, the analysis was carried out in two phases: (1) analysis of the general characteristics of the Defense White Papers by the USA and China; and (2) comparison of discourses conveyed in chapters on international cooperation in each Defense White Paper. Speech patterns were analyzed according to rationales of Lexical Semantics and Critical Discourse Analysis. As a result, elements of semantic fields, intertextuality and modality in discourse were pointed out as parameters that could contribute to the evaluation of cooperation and deterrence/dissuasion actions to be adopted by the USA and China in the 21st century.

Longyue Wang, Zefeng Du, Donghuai Liu et al.

Modeling discourse -- the linguistic phenomena that go beyond individual sentences, is a fundamental yet challenging aspect of natural language processing (NLP). However, existing evaluation benchmarks primarily focus on the evaluation of inter-sentence properties and overlook critical discourse phenomena that cross sentences. To bridge the gap, we propose Disco-Bench, a benchmark that can evaluate intra-sentence discourse properties across a diverse set of NLP tasks, covering understanding, translation, and generation. Disco-Bench consists of 9 document-level testsets in the literature domain, which contain rich discourse phenomena (e.g. cohesion and coherence) in Chinese and/or English. For linguistic analysis, we also design a diagnostic test suite that can examine whether the target models learn discourse knowledge. We totally evaluate 20 general-, in-domain and commercial models based on Transformer, advanced pretraining architectures and large language models (LLMs). Our results show (1) the challenge and necessity of our evaluation benchmark; (2) fine-grained pretraining based on literary document-level training data consistently improves the modeling of discourse information. We will release the datasets, pretrained models, and leaderboard, which we hope can significantly facilitate research in this field: https://github.com/longyuewangdcu/Disco-Bench.

Mariana Souto-Manning

N. Yu. Vidineeva

The article is devoted to the analysis of various aspects of social relations of the late Soviet period, which found expression on the pages of the children’s magazine “Koster”. The novelty of the study lies in the fact that for the first time a model of social relations mediated by the Soviet media was built. The material of the study was the issues of the magazine “Koster” for 1970—1980 in the amount of 6 copies of 52 to 68 pages each. The study was carried out in line with discourse analysis using the techniques of rhetorical analysis. In the course of the study, aspects of social relations reflected in the magazine “Koster” and the topoi realizing them were identified. A classification of aspects of relations and their topoi is proposed: the relationship of a child with an adult (topoi of parental authority, upbringing, approval of a child by an adult), the relationship of a child and peers (a topos of mutual assistance), the relationship “child-state” (topoi of morality, patriotism, ideological education, state care about childhood), man and country (topoi of ideologization, patriotism, mutual assistance, labor, industrialization), interaction between man and nature (topoi of colonization, care, admiration), a person in a multinational country (topoi of friendliness), a country in the world (topoi of superiority, internationalism).

Prisca Piccirilli, Sabine Schulte im Walde

Research on metaphorical language has shown ties between abstractness and emotionality with regard to metaphoricity; prior work is however limited to the word and sentence levels, and up to date there is no empirical study establishing the extent to which this is also true on the discourse level. This paper explores which textual and perceptual features human annotators perceive as important for the metaphoricity of discourses and expressions, and addresses two research questions more specifically. First, is a metaphorically-perceived discourse more abstract and more emotional in comparison to a literally-perceived discourse? Second, is a metaphorical expression preceded by a more metaphorical/abstract/emotional context than a synonymous literal alternative? We used a dataset of 1,000 corpus-extracted discourses for which crowdsourced annotators (1) provided judgements on whether they perceived the discourses as more metaphorical or more literal, and (2) systematically listed lexical terms which triggered their decisions in (1). Our results indicate that metaphorical discourses are more emotional and to a certain extent more abstract than literal discourses. However, neither the metaphoricity nor the abstractness and emotionality of the preceding discourse seem to play a role in triggering the choice between synonymous metaphorical vs. literal expressions. Our dataset is available at https://www.ims.uni-stuttgart.de/data/discourse-met-lit.

Wei Xiang, Bang Wang

A discourse containing one or more sentences describes daily issues and events for people to communicate their thoughts and opinions. As sentences are normally consist of multiple text segments, correct understanding of the theme of a discourse should take into consideration of the relations in between text segments. Although sometimes a connective exists in raw texts for conveying relations, it is more often the cases that no connective exists in between two text segments but some implicit relation does exist in between them. The task of implicit discourse relation recognition (IDRR) is to detect implicit relation and classify its sense between two text segments without a connective. Indeed, the IDRR task is important to diverse downstream natural language processing tasks, such as text summarization, machine translation and so on. This article provides a comprehensive and up-to-date survey for the IDRR task. We first summarize the task definition and data sources widely used in the field. We categorize the main solution approaches for the IDRR task from the viewpoint of its development history. In each solution category, we present and analyze the most representative methods, including their origins, ideas, strengths and weaknesses. We also present performance comparisons for those solutions experimented on a public corpus with standard data processing procedures. Finally, we discuss future research directions for discourse relation analysis.

Carl Philipp Gierlich, Rafał J. Wilhelm Riedel

The authors of this paper provide a critical analysis of the most prominent theoretical vehicles employed in studying differentiated integration in contemporary, post-Brexit Europe. They discuss the descriptive, explanatory, and interpretative potential of the selected theoretical approaches that are applied at the intersection of disintegration and European differentiation discourse. “The holy grail” of the theorising of the dynamic (and accelerating) processes of (dis)integration and differentiation remains undiscovered. Nevertheless, a constant search for theoretical explanation is needed in the in-depth analyses of the current state of the European Union.