English News Research Articles

The Study of Metaphor Translation in News English from the Perspective of Relevance Translation Theory

With rapid globalization, people need to read news reports every day to keep pace with the changing world. News, especially English news, has become one of the most effective and fastest means to disseminate information in different languages and cultural backgrounds. The metaphor, as an essential rhetorical method, is always used in news English. The correct understanding of metaphors in the news plays a crucial role in obtaining news information accurately. Therefore, it is necessary to achieve the optimal metaphor translation in news English, so as to promote communication and mutual understanding between different cultures. This paper adopts the relevance translation theory as the theoretical basis. Firstly, the research on the translation of metaphor and news English is reviewed. Secondly, the overview and applicability of the relevance translation theory are elaborated. Finally, under the guidance of this theory, this paper analyzes the three translation methods adopted by translators in translating metaphors, based on the specific examples of news English.

‘Well, our goal is to achieve sustainable quiet and security for our people’: Negotiating calls for ceasefires in the Gaza war of 2014 in mainstream English news media by Israeli spokespersons

In this study, we examined how calls for ceasefires were negotiated by Israeli spokespersons in mainstream English news media during one instance of the Israeli-Palestinian conflict: the Gaza War of 2014. Recent scholarship in peace studies has begun to examine how ‘peace’ and ‘violence’ are variously constructed to advance vested positions. We extend this focus through a discursive psychological examination of another ostensibly desirable outcome in conflicts, namely ‘ceasefire’. Findings show that the desirability of ceasefires and their negotiation is closely bound to the management of stake and interest by both media persons and Israeli spokespersons. The latter neither explicitly rejected nor accepted calls for ceasefires. Instead, they downgraded ceasefires in favour of other versions of cessation of conflict, framed as ‘sustainable peace’. This allowed for the non-acceptance of ceasefires while, paradoxically, justifying ongoing and further military actions in Gaza. Together, these findings point to the relevance of psychological practices of justifying, managing stake, and avoiding seeming committed to violence in talking about conflict and peace. Findings are discussed in relation to research in peace and political psychology, and implications for the ongoing attacks on Gaza.

Correction: Framing the shooting of Al Jazeera journalist Shireen Abu Akleh in English and Arabic news headlines: a critical discourse study

Correction: Framing the shooting of Al Jazeera journalist Shireen Abu Akleh in English and Arabic news headlines: a critical discourse study

Translated knowledge: the production of marginalization of the Roma during the COVID-19 pandemic

Abstract This research evaluates news during the first year of the coronavirus disease 2019 pandemic in order to examine how European publics made sense of the Roma, the largest and most marginalized ethnic community on the continent. Based on the analysis of news in English, French, Italian, and Romanian, the study finds that much of the coverage across national and cultural boundaries translated marginalization, already exacerbated by the health crisis, through familiar lenses of blame and backwardness. Whereas anti-ethnic discrimination was primarily recognized in news reliant on official, governmental sources, news in French and Italian chiefly circulated representations of otherness. Romanian news revealed both systemic racism and community solutions, also filtered through a discriminatory lens. Translating justice- or equity-oriented content—directly or ideologically—appeared incomplete, infrequent, and often filtered through bias. The tool of local and regional translation emerged as central to the circulation of ideas across the continent.

The Argumentative Structural Shifts in English-Arabic Translation of BBC News Reports: A Sociolinguistic And Cultural Perspective

The socio-cultural and ideological features of translation have become so prominent that many studies have investigated the translation shifts resulting from the linguistic transference from the source language (SL) into the target language (TL). It is the primary aim of the present article to demonstrate the nature of the structural (morpho-syntactic) shifts in the Arabic written translation of English political British Broadcasting Corporation (BBC) news, which may be attributable to differences in cultural, ideological and sociolinguistic backgrounds of the target language speakers (Arabic). The heterogeneity of the socio-cultural context in which the English and Arabic political discourses are communicated is reflected in the syntactic shifts in the sentential and phrasal structure in the translation process of BBC News. The target texts (TT) exhibit a substantial tendency to conform to the socio-cultural and ideological context of the target language (TL) at the expense of the morphosyntactic structure of the ST. The research methodology involves analyzing some English BBC news extracts (ST) and their Arabic translation to investigate the structural shifts in translation. The data analysis, conducted with meticulous attention to detail, shows that the Arabic translation alters the (ST) linguistic structures and shifts the socio-cultural and ideological conceptions of the target text (TT) readers. A key aspect of the methodology is the adoption of a comparative approach, which allows for a thorough examination of the structural differences and similarities and their corresponding content. The article also takes cognizance of Hatim's (1997) ideas on translation across different cultures and those of Fishman (1972) on the sociolinguistic reflections of language. The study concludes that the target readers' cultural background, ideological make-up and sociolinguistic perception can be altered due to the structural shifts in translating political news into Arabic. It shows that the structural shifts lead to specific deviations in the argumentative structure of the source text (ST) through argumentation or counter-argumentation.

Linguistic Features and Translation of English Sports News

Abstract Sports, as one symbol of social civilization, are indispensable to our social and cultural life. Nowadays, with the development of society and the economy, sports news occupies a significant place in our daily lives and has aroused people’s attention. English sports news, as a kind of special news style, has the characteristics of news style but also presents its unique language features. This paper tries to analyze the linguistic features of English sports news with vivid examples of vocabulary and sentences. It includes five parts. The first part introduces the origin and development of English sports news. The second is the literature review, which introduces previous studies on English Sports News and its translation. Then, the third and fourth parts study the linguistic features and translation of English sports news with vivid examples. Lastly, the fifth part is the conclusion, which summarizes the linguistic features of English Sports News and gives several translation principles. For instance: faithfulness, expressiveness, and elegance; literal translation, and free translation. Keywords: English sports news; linguistic features; translation methods

Framing the shooting of Al Jazeera journalist Shireen Abu Akleh in English and Arabic news headlines: a critical discourse study

The study aims to investigate the ideological manipulation of language within news media headlines regarding the killing of Palestinian journalist Shireen Abu Akleh. This study employs Van Dijk’s socio-cognitive model of Critical Discourse Analysis to conduct a comparative qualitative analysis of 100 Arabic and 100 English news headlines, exploring linguistic features at both microstructure and macrostructure levels. The analysis delves into identity construction for the victim and perpetrator, revealing the discursive linguistic manifestations of underlying ideologies in two distinct linguistic and cultural contexts. The microstructure analysis reveals the preference of Arabic headlines for active voice structure, rhetorical devices of numeration, emphatic language, and metaphors. On the other hand, English headlines preferred the passive voice structure and the rhetorical device of litotes. The macrostructure analysis unravels the cognitive interplay between news media outlets and their target audience, demonstrating how selective headlines significantly impact public opinion. It shapes their comprehension and interpretation, particularly within the broader context of the Israeli-Palestinian conflict. Ultimately, this research highlights the dynamic interplay between language, ideology, and power in news media, offering key recommendations for understanding their comparative impacts.

The Vague Expressions in English News Headlines and Film Works

The Vague Expressions in English News Headlines and Film Works

The Application of Zwart’s Shift Model to the English-Arabic Translation of Technology News

The present research paper discusses the applicability of Zwart’s “Comparative–Descriptive Model of Translation Shifts” (1984) to translating Sci-Tech news from English into Arabic. This comprehensive model primarily investigates the translation shifts occurring in literary discourse. To the best of the reCsearcher’s knowledge, very few attempts have been made to apply this shift analysis-based model to the translation of other types of discourse. The paper applies this model to translating scientific discourse, particularly technology news, on several reliable news websites, such as BBC and CNBC. English news on the English websites (ST) and the Arabic translation (TT) on these websites are used as data for the comparative and descriptive analysis. Zwart’s comparative model describes the translation shifts on the microstructural level by comparing the architranseme and the SL and TL transoms. The descriptive model provides insights into the shifts on the macrostructural level. The paper concludes that shifts occurring in the translation of Sci-Tech news can be best described, analysed, and categorised within Zwart’s comparative model. It also concludes that shifts on the microstructural level do not necessarily lead to a noticeable macrostructural change in the primary language metafunctions (interpersonal, ideational, and textual) as in the translation of literary works. Syntactic shifts may, however, result in a macrostructural change of the textual metafunction, which alters the propositional content in some instances.

Critical Discourse Analysis of Brunei's English Mainstream Media Perceptions on the Laos-China Railway

This study aims to comprehensively analyze the coverage of the Laos-China Railway project by mainstream media in Brunei and its impact on public perception. Utilizing web scraping tools such as Octoparse and Heritrix, this paper systematically collected relevant reporting data from two major English news platforms in Brunei, the Borneo Bulletin and Brunei News. Quantitative tools such as AntConc and Excel were employed to process and analyze the data, revealing the frequency, temporal distribution, and emotional orientation of the reports. The qualitative component utilized Critical Discourse Analysis (CDA) to delve into the language use, discourse structure, and implicit meanings within the reports, further exploring how the media shapes the social reality and public cognition of the Laos-China Railway topic.The findings indicate that Brunei media not only frequently and comprehensively report on the Laos-China Railway project but also cover the project's political, economic, environmental, and social implications from multiple perspectives. Additionally, the media coverage reflects a keen awareness of the project's potential risks and a positive appraisal of its developmental benefits. The novelty of this research lies in its mixed-methodological approach and unique perspective on public opinion cognition, providing insights for policymakers on public opinion guidance and project promotion, as well as offering a rich theoretical and methodological reference for the academic community.

Contrasting Linguistic Patterns in Human and LLM-Generated News Text

We conduct a quantitative analysis contrasting human-written English news text with comparable large language model (LLM) output from six different LLMs that cover three different families and four sizes in total. Our analysis spans several measurable linguistic dimensions, including morphological, syntactic, psychometric, and sociolinguistic aspects. The results reveal various measurable differences between human and AI-generated texts. Human texts exhibit more scattered sentence length distributions, more variety of vocabulary, a distinct use of dependency and constituent types, shorter constituents, and more optimized dependency distances. Humans tend to exhibit stronger negative emotions (such as fear and disgust) and less joy compared to text generated by LLMs, with the toxicity of these models increasing as their size grows. LLM outputs use more numbers, symbols and auxiliaries (suggesting objective language) than human texts, as well as more pronouns. The sexist bias prevalent in human text is also expressed by LLMs, and even magnified in all of them but one. Differences between LLMs and humans are larger than between LLMs.

MMHFND: Fusing Modalities for Multimodal Multiclass Hindi Fake News Detection via Contrastive Learning

Multimodal content contains more deception than unimodal information, causing significant social and economic impacts. Current techniques often focus on single modality, neglecting knowledge fusion. While most studies have concentrated on English fake news detection, this study explores multi-modality for low-resource languages like Hindi. This work introduces the MMHFND model, based on M-CLIP, which uses late fusion for coarse (Fake vs Real) and fine-grained (World vs India vs Politics vs News vs Fact-Check) configurations. We extract deep representations from image and text using image transformer ResNet-50, a BERT-based L3cube-HindRoberta text transformer handling headlines, content, OCR text, and image captions, paired M-CLIP transformers, and an ELA (Error Level Analysis) image forensic method incorporating EfficientNet B0 to analyse multimodal news in Hindi language based on Devanagari script. M-CLIP integrates crossmodal similarity mapping of images and texts with retrieved multimodal features. The extracted features undergo redundancy reduction before being channelled into the final classifier. The MAM (Modality Attention Mechanism) is introduced, which generates weights for each modality individually. The MMHFND model uses a computed modality divergence score to identify dissonance between modalities and a modified contrastive loss on the score. We thoroughly analyse HinFakeNews dataset in a multimodal context, achieving accuracy in coarse and fine-grained configurations. We also undertake an ablation study to evaluate outcomes and explore alternative fusion processes on three different setups. The results show that the MMHFND model effectively detects fake news in Hindi with an accuracy of 0.986, outperforming other existing multimodal approaches.

Artificial Intelligence Applications in English News Translation: Strategies and Research

The rapid advancement of Artificial Intelligence (AI) is significantly transforming the field of translation, especially in English news translation. This paper aims to explore the application strategies of AI in English news translation and its current research status. First, it introduces the basic concepts of AI and machine translation, reviews the development history of machine translation technology, and analyzes the unique requirements of news translation. Then, it discusses key application strategies of AI in news translation, including data collection and processing, model selection and training, translation quality assessment, and real-time translation technology. Additionally, the paper analyzes major challenges in AI applications, such as translation accuracy, cultural and contextual understanding, and data privacy and security, and proposes corresponding solutions. Finally, it explores future development trends in AI translation technology, including technological advancements, application expansion, and ethical and societal impacts. This paper aims to provide comprehensive references and in-depth insights for researchers in related fields.

Weathering Words: A Cross-linguistic Analysis of Metaphorical Frames of Climate Change Discourse in English and German Online News Outlets

Climate change is undeniably a major challenge of the 21st century and those to come. While scientists agree on this, the public does not as opinions range from climate change deniers to those who devote their lives to fighting it. News media play a crucial role in shaping the public’s opinion on climate change and as they have a significant influence on the public discourse. This paper offers a cross-linguistic comparison of conceptual climate change metaphors in online news outlets from four different countries and two languages. Using conceptual metaphor analysis, I identified prevalent source domains to conceptualize the causes, effects, and responses to climate change. Overall, I found three shared metaphorical frames: war, destination, and construction, as well as additional metaphors in each language. The analysis revealed war and destination as most dominant source domains through which effects and responses are framed. This suggests news media emphasize the severity of effects and urgency to respond through creating a threatening image of climate change, which we do have the power to fight. The pervasive use of these two metaphors in both languages suggest a common understanding climate change.

Transediting the terms used for describing the US dollar in Arabic and English news websites: a triangulational study of transediting strategies adopted in four channels

News translation, which is often known as news transediting, which is concerned with the translation of materials for print and/or online mass media, has long stimulated interest within the field of translation studies. The present paper explores the strategies of transediting the terms used for describing the US dollar in the websites of four news channels, namely: Alarabiya, Aljazeera, BBC and CNN between Arabic and English from the first of July 2022 to the thirty-first of December 2022. The paper conducts triangulational analysis to identify the type and frequency of the transediting strategies used in each channel, present how certain examples of each strategy are adopted and demonstrate the similarities and differences in the type and frequency of the transediting strategies employed between the channels. The paper argues that the choice of the transediting strategy is based on the type of audience, context, the channel’s policy and the type of information intended for delivery. This paper offers a baseline for exploring the strategies of transediting the terms used for describing the US dollar in both Arabic and English news websites of the aforementioned channels, which may have implications for exploring other transediting strategies used in other news websites of different channels broadcast in different languages.

Breaking Bad News in Medical English

Breaking Bad News in Medical English

Power implicit in language: A case of the Sri Lankan Government’s securitization of the COVID-19 pandemic

This is an attempt to uncover the ways in which the Government of Sri Lanka (GoSL) securitized its response to the COVID-19 pandemic in 2020, and how that process of securitization functioned in furthering its overall approach to its legitimacy and governance. Regarding this, the article focuses on the language which characterizes the discourse and the references made to the Sri Lankan armed conflict. Using Critical Discourse Analysis (CDA), written and audio-visual texts from government media outlets, private media outlets, and English news articles are assessed in this article as primary data. The discourse on the studied corpus reveals how the State securitized its response to COVID-19 as one befitting a military threat rather than a public health risk. In militarizing the securitizing process, the police and triforces were deployed as first respondents. References made to their experience in defeating the Liberation Tigers of Tamil Eelam (LTTE), as well as to former President Gotabaya Rajapaksa’s role as Defence Secretary in the armed conflict against the LTTE, were used in legitimizing the process of securitization. References made to the “sacrifices” made by the State, and the military, by extension, were used in instilling a sense of responsibility among the public to show their support for the government in its attempt to grapple with the pandemic. The public were also asked to act in unison which included an implied demand for discarding their diverse identities. This article further reveals a novel form of religious securitization emerging from the studied corpus in relation to GoSL’s response to the COVID-19 pandemic.

The link between translation difficulty and the quality of machine translation: a literature review and empirical investigation

We survey the relevant literature on translation difficulty and automatic evaluation of machine translation (MT) quality and investigate whether source text’s translation difficulty features contain any information about MT quality. We analyse the 2017–2019 Conferences on Machine Translation (WMT) data of machine translation quality of English news text translated to eleven different languages (Chinese, Czech, Estonian, Finnish, Latvian, Lithuanian, German, Gujarati, Kazakh, Russian, and Turkish). We find (weak) negative correlation between the source text’s length, polysemy and structural complexity and the corresponding human evaluated quality of machine translation. This suggests a potentially important but measureable influence of source text’s translation difficulty on MT quality.

Improving ROUGE‐1 by 6%: A novel multilingual transformer for abstractive news summarization

SummaryNatural language processing (NLP) has undergone a significant transformation, evolving from manually crafted rules to powerful deep learning techniques such as transformers. These advancements have revolutionized various domains including summarization, question answering, and more. Statistical models like hidden Markov models (HMMs) and supervised learning have played crucial roles in laying the foundation for this progress. Recent breakthroughs in transfer learning and the emergence of large‐scale models like BERT and GPT have further pushed the boundaries of NLP research. However, news summarization remains a challenging task in NLP, often resulting in factual inaccuracies or the loss of the article's essence. In this study, we propose a novel approach to news summarization utilizing a fine‐tuned Transformer architecture pre‐trained on Google's mt‐small tokenizer. Our model demonstrates significant performance improvements over previous methods on the Inshorts English News dataset, achieving a 6% enhancement in the ROUGE‐1 score and reducing training loss by 50%. This breakthrough facilitates the generation of reliable and concise news summaries, thereby enhancing information accessibility and user experience. Additionally, we conduct a comprehensive evaluation of our model's performance using popular metrics such as ROUGE scores, with our proposed model achieving ROUGE‐1: 54.6130, ROUGE‐2: 31.1543, ROUGE‐L: 50.7709, and ROUGE‐LSum: 50.7907. Furthermore, we observe a substantial reduction in training and validation losses, underscoring the effectiveness of our proposed approach.

Manipulating English news reports translated into Arabic: the influence of patronage and socio-narratives

ABSTRACT The influence of patronage and socio-narratives on the process and product of translation is examined, focusing on the implications of these social phenomena on the translation of current affairs in the Arab world. The translation of English news articles in Arabic newspapers is analysed to see whether the linguistic structures and strategies employed by the source and target newspapers in news reporting are doctored for communication flow or rather censored to serve the patrons’ goals and political agendas. It is suggested that the main objective in translating news articles into Arabic is not to achieve linguistic equivalence in all cases, but rather to naturalize the message in the communication process or employ censorship in order to fulfil the needs and political agenda of the ‘patrons’. The selected translated news, from two popular Arabic newspapers, the Egyptian Al-Ahram and the Syrian Tishreen, feature various manipulative strategies that shift perspectives and justify the repressive measures and policies of the Arab patrons.