ABSTRACT This study aimed to examine the effect of climate change anxiety on women’s reproductive health protective attitudes and to identify the factors associated with both constructs. This descriptive and relational study was conducted with 789 women aged 18–49 years who were admitted to a public hospital in the Central Anatolia region between July 2023 and March 2024. Data were collected using the Personal Information Form, the Climate Change Worry Scale (CCWS), and the Married Women’s Reproductive Health Protective Attitudes Scale (RHPAS). Descriptive statistics, independent samples t-test, one-way ANOVA, and multiple linear regression analyses were used to analyze the data. The mean age of the participants was 32.65 ± 7.83 years. The mean RHPAS and CCWS scores were 141.64 ± 21.58 and 29.77 ± 10.49, respectively. Regression analysis showed that educational level, family type, employment status, smoking, use of family planning methods, following climate change related news, and climate change anxiety were significant predictors of reproductive health protective attitudes. In addition, perceived income level, following climate change related news, and reproductive health protective attitudes were identified as significant predictors of climate change anxiety. Furthermore, climate change anxiety and reproductive health protective attitudes were found to be significant predictors of each other. The findings suggest that climate change anxiety significantly predicts women’s attitudes towards protecting their reproductive health and that these attitudes significantly predict climate change anxiety. To our knowledge, this is the first study to demonstrate the effect of climate change anxiety on women’s reproductive health protective attitudes, thereby providing novel evidence and contributing to the existing literature.

Digital transformation has transformed the tourism promotion landscape through social media, travel marketplaces, and user-experience-based content. Visual, interactive, and rapidly disseminating digital information shapes destination images and influences traveler decisions. However, the lack of digital promotion standards allows for misleading promotional practices, visual manipulation, false testimonials, and exaggerated claims that have the potential to harm tourism consumers. This study aims to analyze the synchronization between the Electronic Information and Transactions Law (UU ITE) and the Tourism Law in overseeing digital tourism promotion. This study uses a normative juridical method with a legislative approach and a literature review of academic documents and related news. The research findings show that while both laws share the same goal of ensuring accurate and non-misleading information, neither provides specific technical standards for digital promotion. The ITE Law is general and does not specifically regulate tourism promotion, while the Tourism Law was drafted before social media became a primary tool for destination marketing. This has resulted in regulatory gaps and fragmented authority between the Ministry of Communication and Informatics, the Ministry of Tourism and Creative Economy, BPKN, and BPSK. This research recommends the establishment of derivative regulations related to digital promotion, ethical content standards, advertising transparency, and an integrated cross-agency oversight mechanism to protect tourism consumers in the digital space.

The agrarian conflict in the Kendeng Mountains, Central Java, arose due to the construction of a cement factory by PT Semen Indonesia, which was supported by the state through the issuance of environmental permits, even though several permits had been revoked by court decisions. The local community rejected the project because it threatened their water sources, environment, and livelihoods. This study aims to analyze the relationship between the state, corporations, and civil society in the agrarian conflict in Kendeng, as well as the role of civil society movements in responding to this conflict. The research uses qualitative methods with a literature study of journals and related news reports. Using Karl Marx's theory of conflict and Charles Tilly's theory of social movements, the study finds that the state tends to side with corporate interests, while civil society continues to build resistance through collective action, legal advocacy, public campaigns, and ecofeminist movements. The Kendeng conflict reflects the imbalance of power relations and the weakness of substantive democracy in natural resource management. The need for increased transparency in licensing, community participation, and ecological protection are the main solutions.

This study investigates the compliance of Indonesian national (Sindonews.com) and local (Poskota.co.id) online media with the Child Friendly Journalism Guidelines (PPRA) issued by the Indonesian Press Council and UNICEF’s standards on reporting about children. Using quantitative content analysis of 282 child related news articles published in January, June, and December 2024, the research applied 117 indicators grouped into five dimensions: identity protection, language and narrative sensitivity, visual and audio representation, privacy and digital traces, and legal and journalistic ethics. The findings show partial compliance: both outlets performed relatively well in protecting children’s privacy and digital traces but scored lowest in legal and ethical compliance. Identity protection and language sensitivity reflected only moderate adherence, with repeated issues such as disclosure of sensitive details and sensationalist framing across outlets. Statistical analysis confirmed no significant differences between national and local media, suggesting systemic challenges rather than outlet specific weaknesses. These results highlight tensions between commercial pressures and child rights standards in Indonesian journalism. By applying social responsibility theory and framing analysis, this study contributes to scholarly debates on ethical reporting and emphasizes the need for stronger institutional enforcement, participatory monitoring, and trauma informed approaches to safeguard children’s rights in media coverage.

With the rapid growth of online Bangla news portals, thousands of articles are published daily on similar topics, resulting in an information overload for readers. Existing recommendation systems mostly focus on personalized suggestions based on user history, whereas readers frequently desire related news on a given topic across multiple sources. This challenge is amplified by the scarcity of robust Bangla Natural Language Processing (NLP) tools and the heterogeneous structure of news content. In this regard, we introduce TopicMap-BN, a scalable and explainable topic-based framework for cross-source Bangla news recommendation system. This system integrates Bangla-specific preprocessing with neural topic modeling (BERTopic with transformer embeddings), near-duplicate detection (MinHash and SimHash), and diversityaware re-ranking (MMR, xQuAD, DPP). These components facilitate coherent story grouping, interpretable topic labels, and recommendations that maintain relevance, freshness, and diversity. The effectiveness of the proposed framework is demonstrated by experiments carried out on the Potrika corpus (approximately 665,000 articles) and live crawls from five popular news portals. As a result, the system achieved topic quality, demonstrated by an NPMI score of 0.62 and a human agreement value (κ) of 0.71. In terms of story deduplication, it secured a precision of 0.91 and an F1-score of 0.88, indicating reliable clustering of nearduplicate articles. Moreover, the framework demonstrates its ability to produce precise and well-ranked recommendations by having a precision at rank five of 0.72 and an NDCG at rank five of 0.75. Compared with classical baselines such as TF–IDF with cosine similarity, TopicMap-BN achieves substantial gains across coherence, ranking, and diversity. These findings confirm the feasibility of cross-source Bangla news recommendation and emphasize the significance of domain-specific NLP frameworks in low-resource settings.

BackgroundRespiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infections in children younger than 5 years of age. Given the high morbidity and mortality associated with RSV in India, the introduction of a vaccine against RSV will potentially reduce the disease’s burden. However, vaccine acceptance is influenced by public perception, which is shaped by information disseminated through media sources. This study aims to explore the landscape of RSV–related news coverage in India’s digital media.ObjectiveThis study aims to conduct a comprehensive content analysis to explore the landscape of RSV–related news coverage in India’s digital media.MethodsMedia content analysis was retrospectively conducted by a digital search for all related news pieces in the trustworthy brands of 4 trusted newspapers (Hindustan Times, The Hindu, The Indian Express, and The Times of India) and 3 news channel websites (India Today, NDTV news, and News 18), between November 1, 2022, and October 31, 2023. A total of 58 news pieces were retrieved using selected keywords, with inclusion criteria encompassing English-language news pieces with RSV–specific content. Two reviewers compiled, coded, and analyzed the content. Quantitative data were analyzed descriptively, while qualitative content analysis assessed the emotional tone and sentiment of the pieces.ResultsThe findings revealed significant digital media coverage on RSV infection and the potential vaccines. The majority of news pieces (53/58, 91%) discussed RSV signs and symptoms, with 64% (37/58) addressing the disease severity and 36% (21/58) highlighting its seasonal surge. However, only 5% (3/58) focused on diagnostic aids. Additionally, 41% (24/58) of news pieces discussed RSV in the context of COVID-19. Regarding the vaccine, 29% (17/58) of news pieces mentioned it, with 26% (15/58) highlighting manufacturers such as Pfizer and GlaxoSmithKline (GSK). Positive sentiment was found in 35% (20/58) of news pieces, while 43% (25/58) exhibited negative sentiment, often related to the disease burden and severity. Emotional tone analysis revealed that 74% (43/58) of news pieces contained emotional elements, with 58% (25/43) expressing negative emotions (eg, concern and anxiety), particularly about hospitalizations and deaths. In contrast, a positive tone was emulated in the frequent mentions of the RSV vaccines as safe, effective, and approved.ConclusionsThe analysis revealed significant coverage of RSV–related news in India’s digital media, with a focus on disease severity and hospitalizations. While positive sentiment was expressed in coverage of the RSV vaccine, negative sentiments dominated discussions on the disease burden. However, considering the limited number of news pieces, the study highlights the need for improved media coverage to raise awareness about the disease and its preventive strategies. Further research should explore the implications of the overlap between RSV and COVID-19 in media coverage and the limited focus on RSV diagnostics, with a focus on understanding how these factors impact public health outcomes.

Abstract Most investors view the stock market as a place to trade shares where everything is well organized based on data and economic factors. Investors behave rationally after analyzing the market based on related data and news. However, behavioural finance shifts the traditional view, emphasising human emotions and cognitive bias. This review explores the intersection of how human psychology, such as cognitive bias, emotion, and cultural background, drives retail investment decisions across global stock markets in the past decades. Important psychological aspects of loss aversion, overconfidence, herding, and anchoring are examined to determine how they affect market efficiency and the pricing of assets. In the review, we outline the evolution, strengths, and weaknesses of financial theory and models from classical paradigms like efficient markets hypothesis, CAPM, through behavioral finance to emerging neurofinance. In addition, we consider both qualitative and quantitative research concerning cognitive and emotional biases, such as overconfidence, herding, and loss aversion, which influence the decision-making processes of individual investors. By combining empirical data and theoretical models, this review offers a better view of the reasons behind unregulated markets and the role played by cognitive biases in destabilizing the market. The findings point to applying approaches in investment and financial literacy to help curb the detrimental effects of irrational thinking. Keywords: Behavioral Finance, Cognitive Biases, Investor Psychology, Stock Market Investments, Market Volatility, Loss Aversion, Overconfidence, Herd Behavior, etc.

Indonesia, as an archipelagic nation with a vast marine territory, possesses significant coral reef ecosystems, including those in Bengkulu. However, the condition of coral reefs in Bengkulu has suffered damage due to human activities and natural factors. The Bengkulu Provincial Government issued Regional Regulation Number 5 of 2019 concerning the Management of Coastal Areas and Small Islands as a protective measure, but the effectiveness of its implementation has not been optimal. This research aims to analyze the implementation of this Regional Regulation in the protection of coral reefs in Bengkulu waters and to identify the challenges faced. Employing a normative legal research method with a literature study, this research examines laws and regulations, scientific literature, and related news. The research results indicate that the Regional Regulation has been implemented through various programs such as coral reef restoration, marine debris management, and socialization. However, its effectiveness is hampered by a lack of public awareness, weak supervision, and insufficient control over tourism activities. Further efforts are needed to increase public awareness, strengthen supervision, and manage tourism activities sustainably to achieve effective coral reef protection in Bengkulu.

This study examined the role of an English national daily; The Hindu, on communal riots of Delhi happened on February 2020 and how this newspaper shaped the public perception during this time. This research highlighted the various angles prepared by researchers; which had been adopted by the paper at the time of reporting these kinds of sensitive matters. By doing corpus analysis through AntConc; of news riots related news from the web edition of the national newspaper; The Hindu. Researcher collected the data to analyze the variables like size of news, usage of images, framing, tone of content, and sources included in news. The findings of this paper shows that while sometimes newspaper dailies prepare an objective narrative but most of the times it crafts the reality which aligns with different political ideologies.

The purpose of this study is to explore and compare the key topics related to issues in para-sports and related policies through objective information analysis and topic modeling using text mining techniques. To achieve this, we selected the BIGKINDS media database for news data and the first to third editions of the Long-Term Development Plan for Disability Sports for policy-related documents. The results of this study are as follows: First, the main keywords related to para-sports policies were found to be: community sports, participation encouragement, public sports facilities, sports services, information sharing and smart technology. The key keywords in related news issues were: Disability Sports Association, para-sports for daily life, sports competitions, sports facilities, disability-specific sports facilities, and relevant laws and policies. Second, the main topics of para-sports policies and related news issues were identified as four main topics. The key topics of policies included: encouraging participation in sports by type, expanding sports services, providing sports services for people with disabilities, encouraging participation in community sports, and building sports organization systems through informatization. The key topics of news issues were: laws and policies regarding disability-specific sports facilities and sports facilities, competition support and athlete development by type, creating stable training environments centered on regional disability sports organizations, and activating disability sports for daily life. Third, the time-series comparative analysis of para-sports policies and news issues showed that in the first phase of the first long-term development plan and news articles, keyword clusters similar to community sports appeared. In the second phase of the second long-term development plan and news articles, keyword clusters centered on community sports and infrastructure, such as sports facilities, were observed. In the third long-term development plan, keyword clusters such as specialized sports, infrastructure, and programs appeared, while in the third phase of news articles, keyword clusters centered on strengthening the capacity of the Disability Sports Association and infrastructure, like sports facilities, were observed.

In the context of the country's promotion of ecological civilization and the fulfillment of the "dual-carbon" goal, green and sustainable development should be an important part of the enterprise's connotative development, rather than just "greenwashing" packaging. Although the research on "greening" related topics has been increasing in China, there are deficiencies in quantitative and systematic research. Therefore, in this paper, by studying the overall trend of articles published on web of science from 2013 to 2023, we use citespace to comprehensively and systematically portray the hotspot evolution law in the field of Green Washing. At the same time, we use python to partition the content of the related news Green Washingwashing text, and with the help of mining methods such as neologism discovery and LDA theme model, we identify the hotspot keywords and carry out the evolutionary analysis. The results show that the greenwashing themes mainly focus on greenwashing behavior, green development, green finance, carbon neutrality, information disclosure, social responsibility, sustainable development and environmental performance. Finally, the paper further outlines the existing research framework on greenwash from the theoretical perspective of sustainable development, and offers a perspective on the future direction of article on greenwash governance from three parts: government, business and individuals.

Purpose This study aims to investigate the influence of U.S. dollar-related news on EUR/US$ exchange rate using a novel hybrid news-fundamentals-based VAR model applied to 18 years of monthly data. Design/methodology/approach Leveraging Latent Dirichlet Allocation (LDA), the authors identify the top 5 U.S. dollar-related news topics, quantify the attention they receive over time using Shannon’s entropy, and integrate these news-generated metrics with news-constructed economic uncertainty indices and Taylor rule fundamentals into the VAR model. Through impulse-response analysis and forecast error decomposition, the authors examine how exchange rates react to shocks from the identified US$-related news topics and economic uncertainty captured by the news. Findings The findings reveal that news related to the US dollar and economic uncertainty account for 29% of long-term EUR/US$ variation. These results are robust, validated through robustness checks, Granger causality tests, sensitivity analysis and applying the same model to the GBP/USD exchange rate. Originality/value Combining news attention metrics with macroeconomic fundamentals enhances exchange rate identification, outperforming the models that rely solely on the Taylor rule or news variables.

Timely and complete administration of multiple-dose vaccines is essential to guarantee the efficacy. Our study aimed to investigate how people’s vaccination attitudes changed over the course of the multi-dose vaccination schedule and comprehensively reported a phenomenon of vaccination burnout. The participants’ vaccination burnout levels were quantified by our designed vaccination burnout scale. This study is a retrospective cross-sectional study. Among 3068 valid participants, 2991 had finished the routine two shots of COVID-19 vaccine, and 2367 had a positive attitude towards the primary doses of vaccination. Notably, 232 participants who previously had a positive attitude towards primary doses of vaccination refused to take additional multiple shots, and another 83 had changed their positive attitudes to negative, despite having taken the third shot. Participants whose attitudes or behaviors had changed had higher scores of vaccination burnout than those who still maintained a positive attitude (Z=−8.491, P < .001). The frequency of actively paying attention to the related disease news, occupation, monthly income, and residence of the participants were key factors associated with the vaccination burnout. Interventions should be implemented to alleviate the exhausted attitudes and improve people’s compliance with vaccination schedules against the future pandemic.

In real life, many dynamic events, such as major disasters and large-scale sports events, evolve continuously over time. Obtaining an overview of these events can help people quickly understand the situation and respond more effectively. This is challenging because the key information of the event is often scattered across multiple documents, involving complex event knowledge understanding and reasoning, which is under-explored in previous work. Therefore, we proposed the Event-Centric Multi-Document Summarization task, which aims to generate concise and comprehensive summaries of a given event based on multiple related news documents. Based on this, we constructed the EventSum dataset, which was constructed using Baidu Baike entries and underwent extensive human annotation, to facilitate relevant research. It is the first large-scale Chinese multi-document summarization dataset, containing 5,100 events and a total of 57,984 news documents, with an average of 11.4 input news documents and 13,471 characters per event. To ensure data quality and mitigate potential data leakage, we adopted a multi-stage annotation approach for manually labeling the test set. Given the complexity of event-related information, existing metrics struggle to comprehensively assess the quality of generated summaries. We designed specific metrics including Event Recall, Argument Recall, Causal Recall, and Temporal Recall along with corresponding calculation methods for evaluation. We conducted comprehensive experiments on EventSum to evaluate the performance of advanced long-context Large Language Models (LLMs) on this task. Our experimental results indicate that: 1) The event-centric multi-document summarization task remains challenging for existing long-context LLMs; 2) The recall metrics we designed are crucial for evaluating the comprehensiveness of the summary information.

ABSTRACTIn China's financial and economic system, the agricultural futures market plays an important role in guiding the market to self regulate and providing efficient information transmission for regulators. The effective prediction of futures prices can assist in guiding agricultural production, monitoring operational risks arising from significant price fluctuations, and enhancing the predictability and pertinence of the country's macroeconomic regulation policies. This study investigates the main variety of grain futures—soybean futures, taking into account complex market and non‐market influencing factors. Using historical market data and related news headlines of soybean futures as source data and integrating topic identification and sentiment analysis techniques, a novel framework for predicting agricultural futures prices that integrates topic sentiment is constructed. This model uses BERTopic to extract topic information from agricultural news texts, then integrates FinBERT to construct topic‐based sentiment features, fuses them with structured market features, and constructs LSTM price prediction model with multi‐feature inputs. In order to better model the short‐term features and state transfer patterns of the time series, hidden Markov model (HMM) is further used to extract the hidden states, which are deeply fused with the LSTM model. The empirical results show that the model fusing topic and sentiment features significantly improves the forecasting accuracy in all lags, LSTM works best in short‐term forecasting, and the combination of HMM and LSTM exhibits significant performance advantages in medium‐ and long‐term forecasting. Compared with the baseline model that relies only on market features, topic sentiment features provide important incremental information for price forecasting, and the contribution of each topic sentiment feature calculated based on the PI metric is close to 50%. In addition, deep learning–based prediction model performs better than baseline machine learning models in dealing with extreme external shocks such as climate disasters, the COVID‐19 pandemic, and the Russia–Ukraine conflict.

Early detection of fake news is crucial to mitigate its negative impact. Current research in fake news detection often utilizes the difference between real and fake news regarding the support degree from reliable sources. However, it has overlooked their different semantic outlier degrees among unreliable source information during the same period. Since fake news often serves idea propaganda, unreliable sources usually publish a lot of information with the same propaganda idea during the same period, making it less likely to be a semantic outlier. To leverage this difference, we propose the Reliable-Unreliable Source Reference (RUSR) Fake News Early Detection Method. RUSR introduces the publication background for detected news, which consists of related news with common main objects of description and slightly earlier publication from both reliable and unreliable sources. Furthermore, we develop a strongly preference-driven support degree evaluation model and a two-hop semantic outlier degree evaluation model, which respectively mitigate the interference of news with weak validation effectiveness and the tightness degree of semantic cluster. The designed redistribution module and expanding range relative time encoding are adopted by both models, respectively optimizing early checkpoint of training and expressing the relevance of news implied by their release time gap. Finally, we present a multi-model mutual benefit and collaboration framework that enables the multi-model mutual benefit of generalization in training and multi-perspective prediction of news authenticity in inference. Experiments on our newly constructed dataset demonstrate the superiority of RUSR.

The fragility of the global financial system, exposed during the 2008 financial crisis, highlighted the challenges posed by the complexity and opacity of the derivatives market. This research examines the application of financial derivatives in risk management, focusing on their role in mitigating risks through the case of JPMorgan Chase during the crisis. The paper focuses on the types and functions of financial derivatives, especially the use of credit default swaps (CDS) and interest rate swaps (IRS) and their risk management applications in financial institutions. Data sources include JPMorgan Chase's public annual report, financial report and related news reports. The results show that JPMorgan Chase has reduced some subprime risk exposure and successfully avoided potential losses of about US$5 billion through the effective use of financial derivatives. However, the use of derivatives also exposes problems such as market liquidity and counterparty risk, reminding financial institutions to carefully manage related risks when using derivatives. In conclusion, while derivatives can be powerful risk management tools, their over-reliance may introduce new systemic risks.

The Free Nutritious Meal Program (MBG) is a strategic initiative aimed at improving the nutritional quality and learning motivation of students in Indonesia. This program is based on data showing that 41% of students in Indonesia experience hunger while studying, which negatively impacts their concentration and academic achievement. MBG not only targets school children, but has also been expanded to include toddlers, pregnant women, and breastfeeding mothers. The government has allocated a budget of IDR 71 trillion in the 2025 Draft State Budget for the initial phase of this program, which is planned to start on January 2,A 2025. Through critical discourse analysis developed by Teun A. Van Dijk, this study will analyze the structure and discourse strategies used in the MBG program, as well as how public perceptions of this program are formed and influence students' attitudes and behaviors at school. Data were collected through non-participant observation in schools involved in the program as well as documentation from various social media and related news. The results of this study are expected to provide a clearer picture of the impact of the program on students' learning motivation and its implications in the context of education in Indonesia. Thus, this study can contribute to the development of more effective education policies in Indonesia.

ABSTRACT More than three years prior to President Jimmy Carter’s “crisis of confidence” speech, letter-to-the-editor writers, cued by daily news coverage, acknowledged an emerging “loss of confidence in the American political ‘system’” prompted by the materialization of “showbiz politics” and superficial, sensationalistic campaign reporting surrounding the presidential horse race. In conversation with cultural studies scholars and historians, this study seeks to better understand lingering material traces of the nation’s structures of feeling bound within newspaper editorial pages during the first post-Vietnam/Watergate presidential election, with a focus on emerging cultural conversations cued by campaign reporting on the candidacy of relatively unknown “outsider” Jimmy Carter. The coauthors conducted a textual analysis of a purposive sample of letters to the editor in the nation’s six leading-circulation newspapers within a ten-month period surrounding the 1976 presidential campaign, read with and against the grain of related news coverage and archival materials. This paper gives us a new understanding of the critique of the American political system and an emerging national malaise during the long seventies.

Cities are recognized as significant contributors to carbon emissions due to their numerous productive enterprises and dense populations. Tackling climate change and achieving the transformation to low-carbon cities requires public participation. News media serve as the primary channel for the public to gain relevant information, and journalists’ attitudes significantly influence the content of related news reports. This study focuses on the attitudes of Chinese journalists towards the transition to low-carbon cities, using semi-structured interviews in qualitative research to conduct 31 interviews with participants from Chinese mainstream medias. This research discussed the journalists’ attitudes towards the low-carbon city transition, based on our samples studies, we find that (1) low-carbon city development brings some new opportunities for economic development and improving the overall urban living environment, despite challenges like lack of professional knowledge and information gaps. (2) Newsworthiness, audience preferences, journalists’ climate scientific knowledge, and management mechanisms, are key factors influencing journalists’ choice to cover low-carbon topics. Despite an increase in related reports, many media do not prioritize climate change or low-carbon transitions as core topics, and environmental news have a life cycle. (3) We suggests that media enhance collaboration with stakeholders, increase audience interaction to learn diverse voices, using storytelling and integrating technological innovations to improve low-carbon communication. This study highlights the complex interactions between media, public participation, and environmental policies, offering a holistic view of the challenges and opportunities in promoting more inclusive and equitable low-carbon city transition.