Journalism Research Articles

News dissemination: a semantic approach to barrier classification

AbstractThe dissemination of information worldwide is significantly facilitated by the news media, with many events having global relevance across various regions. However, certain news events receive limited coverage restricted to specific geographic areas, due to the barriers that hinder the spread of information. These barriers can be attributed to political, geographical, economic, cultural, or linguistic factors. In this research, we propose an approach for classifying these barriers by extracting semantic information from news articles using Wikipedia-concepts. Our methodology involves the collection of news articles, each annotated to indicate the specific barrier types, leveraging metadata from news publishers. Subsequently, we employ Wikipedia-concepts, in conjunction with the content of the news articles, as features to determine the barriers to news dissemination. Our approach is then compared with traditional text classification techniques, deep learning methods, and transformer-based models. We have performed experiments on news articles from ten categories of topics including health, sports, business, etc. The findings indicate that 1) Utilizing semantic knowledge yields distinct concepts across the ten categories, thereby enhancing the effectiveness and speed of the classification model. 2) The proposed approach, incorporating Wikipedia-concepts-based semantic knowledge, leads to improved performance in barrier classification when compared to using solely the body text of news articles. Specifically, there is an increase in the average F1-scores for four out of five barriers, with the economic barrier rising from 0.65 to 0.68, the linguistic barrier from 0.71 to 0.72, the political barrier from 0.68 to 0.70, and the geographical barrier from 0.63 to 0.68.

The “dispersed” market: trading vegetables in Bamberg’s vegetable-growing district

Based on field observations, interviews, personal websites and news publications, the article takes a detailed look at the trade in Bamberg vegetables in a traditional vegetable-growing district. I ask the following questions: why, despite logistical and transportation inconveniences, buying vegetables from the producer is in demand and popular in today’s Germany? what can the convenience of the supermarket be opposed to? and what can sellers who have grown their own produce offer the sophisticated resident of a wealthy German city? The collected field material shows that the sale/purchase of vegetables in the stalls of the “dispersed market” occupies a certain niche in the consumption culture of the city dwellers and realizes a specific, emotionally loaded purchasing experience, which speaks of the eventization of trade. This different experience of consumption is purposefully constructed as if it is rooted in tradition and referring to some idealized past and notions of homeland, as well as to the concept of urban authenticity.

Russian wheat export to African countries at the present stage: key development features, challenges and opportunities

The Russian Federation traditionally remains one of the most important producers and suppliers of wheat on the world market of agricultural raw materials and food. The countries of the African continent, on the contrary, form the largest group of importers and consumers of this product. The purpose of the article is to identify the main trends in the dynamics of Russian wheat exports to African countries at the present stage, as well as to identify the key features of this aspect of trade relations between the parties. The article examines the dynamics and features of export-import relations between the Russian Federation and African countries in the field of international wheat trade through the prism of food security theory. The paper presents an analysis of numerical indicators of wheat production in Russia and its exports to African countries in comparison with indicators of total supplies to the world market, identifies specific features of Russian exports focused on supplies to the African region, and concludes that Russia needs to expand its participation in ensuring food security in the Global South. The source base is statistical materials containing numerical indicators of wheat production and trade by countries and regions of the world, published by international organizations (FAO, International Trade Center, UNCTAD/WTO), national executive authorities, their subordinate institutions and the Bureau of Statistics (Rosstat, USDA); as well as analytical and information and news publications on the topic.

Internet Search Activity for Intentional Self-Harm Forums After a High-Profile News Publication: Interrupted Time Series Analysis.

Searches for "pro-suicide" websites in the United States peaked during the week a high-profile news story was published and remained elevated for 6 months afterward, highlighting the need to avoid mentioning specific sources of explicit suicide instructions in media publications.

AI Ethics in Journalism (Studies): An Evolving Field Between Research and Practice

The integration of artificial intelligence (AI) in journalism has sparked complex ethical debates, particularly with the rise of generative AI systems. By now, AI permeates the entire news cycle, from information gathering to news dissemination, raising questions revolving around issues such as transparency, accountability, responsibility, bias, and diversity. Previous research showed that news organizations have slowly approached and adapted to ethical concerns regarding the use of AI, developing critical stances mainly due to rising AI power, growing audience skepticism, and mounting tensions within the industry between news publishers’ strategies and journalists’ anxieties. Consequently, ethical guidelines have started to emerge in news organizations, but their practical application remains challenging and under-studied, not only due to the opacity of AI algorithms, but also due to the difficulties of “embedding” journalistic values into AI systems. In the light of an intensifying discourse about ethical concerns in the news industry and growing efforts by governments and institutions such as the European Union to strengthen AI governance, journalism studies have started to explore the issue as well. However, research on AI ethics is still in its infancy, with significant gaps in understanding the practical enforcement of ethical guidelines within newsrooms, in particular when it comes to the design of AI systems. This essay critically discusses the way journalism (studies) approach ethical issues related to the use and the design of AI systems, given that the responsible use and design of AI systems in journalism is crucial given its integral role for democracy and society.

Hashtivism as media discourse: analysing pioneer environmental action in Public Service Media through transmedia formula

Various studies have analysed the news media’s participation in debates generated in the public digital sphere represented by social media, particularly X (formerly Twitter) and Instagram. Some of these debates have gained strength and become authentic movements linked to certain social struggles, such as feminism. Similarly, the so-called ‘environmental hashtivism’ or ‘eco-hashtivism’ is acquiring increasing importance in the context of this digital sphere. In view of this, the focus of this article is the distinctive area of public radio and television channels in Europe to examine the transmedia action #EKIN_klima, developed by the Basque public audiovisual corporation Euskal Irrati Telebista (EITB). The study of this pioneering initiative allows us to understand just how the eco-activism actions of the public news media can help to reposition it within the hybrid media system and contribute to the modernisation of its social function, along the lines of other actions, such as those carried out by the BBC against disinformation. In this way, it has been noted that the environmental issue is not only something to be included in the news media agenda, but also an opportunity for the public news media to legitimise its social and innovative character through transmedia branding actions.

A deep learning-based model for stock price prediction under consideration of financial news publishers

Stock price prediction with financial news is beneficial for making correct investment decisions. Recent researches mainly focus on extracting sentiments from news. The publishers as a crucial part of news, which can reflect the authority and reliability, but are usually neglected. In this paper, we propose a novel hybrid deep learning model for stock price prediction that considers the financial news publishers. Specifically, a novel feature about financial news publishers is constructed by classifying financial news publishers. Furthermore, a novel hybrid model named 2d-CNN-AM-LSTM is proposed by combining Convolutional Neural Network (CNN), Attention Mechanism (AM) and Long Short-term Memory, so that it can fuse textual and numerical data in-depth. To verify the superiority of the proposed model, numerous baselines are employed to conduct experiments with representative stock datasets. The experimental analysis demonstrates that the proposed model considering financial news publishers surpasses other benchmarks and achieves outstanding prediction performance.

Parasitic Platforms and the Crisis in News

ABSTRACTThe viability of news has been in rapid decline since the mid‐2000s. This poster presents a critical analysis of how news publishers themselves helped precipitate the crisis by enthusiastically adopting Big Tech platform technologies and audience‐building strategies. I show how search and social media platforms disrupted publishers' relationships with audiences and advertisers by appropriating control over news distribution and revenue. I use Anthony Giddens' structuration theory and Bruno Latour's actor‐network theory to explore the restructuring of the news industry by the sociotechnical practices and surveillance economics of today's dominating platforms. Leveraging Michel Serres' concept of social parasitism, I present a research framework for assessing symbiotic and parasitic relationships in sociotechnical systems using historical, quantitative, and qualitative methods, and to identify where news publishers still have agency to begin resolving the crisis. And I suggest the urgency of this research framework as publishers rapidly adopt new AI technologies.

Living Journals: introducing a new digital participatory approach to study young children’s digital media practices

ABSTRACT Researching daily activities of young children poses methodological challenges that necessitate diverse approaches for effective inquiry. This method article introduces a new digital participatory research method – Living Journals – to study young children's everyday digital media practices at home in Azerbaijan. Employing this method, mothers actively produced multimodal data in situ, responding to prompts sent by the researcher, which were subsequently turned into journals in print and digital formats. Utilising these journals, participant families were encouraged to provide commentary on their respective journals and those of other participating children. This method has a significant capacity to yield metatextual, multivocal, multimodal, and multifunctional data, thereby enhancing the richness of the dataset and facilitating nuanced interpretations. Delving into the intricacies of the method and its advantages, the article elaborates on its procedures, significance, and limitations. The method emerges as a valuable approach, offering a participatory and contextually sensitive means for remotely generating data, allowing researchers to develop textually and visually rich datasets.

GIIE: A Graph-based News Recommendation Model with Intrinsic Interest Enhancement

News recommendation aims to offer potentially interesting news items to a specific user, guided by his historical browsing behaviors. Existing methods failed to effectively address the knowledge sparsity issue that the user may have sparse behaviors and the news may own sparse features. To address the problem, we propose a graph-based news recommendation model with intrinsic interest enhancement, named GIIE , leveraging intrinsic interests and neighbor information to enhance the representation of sparse users and news. Concretely, to fully take advantage of the intrinsic interests, we design an interest encoder based on an interest-type graph with a learnable structure, and explore the interest embeddings from news types. Then we inject the obtained interest embeddings into news, and represent the user by aggregating the clicked news under the same interest and across different interests sequentially. These interests can build a bridge between users, so that users with sparse behaviors can implicitly share knowledge with other users, thereby enhancing their representation. To properly introduce the neighbor knowledge, we propose a graph-based neighbor enhancing mechanism. First, we design a news relation graph and a user relation graph in encoders. Then based on these graphs, we take the attention module to aggregate additional knowledge from neighbors, enhancing sparse news and user representations. To avoid feature ambiguity, we adopt a way to represent the current item (user and news) and its neighbors separately, and then do adaptive aggregation. We evaluate GIIE on the public news recommendation dataset MIND-Large and MIND-Small. Experimental results show that our model can solve the knowledge sparse problem, and outperforms current state-of-the-art models in four indicators.

COVID-19 and Uncertainty Effects on Tunisian Stock Market Volatility: Insights from GJR-GARCH, Wavelet Coherence, and ARDL

This study rigorously investigates the impact of COVID-19 on Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are incorporated. The findings highlight that confirmed and death cases contribute significantly to the escalation in TUNINDEX volatility when using both the conditional variance and the realized volatility. Interestingly, aggregate indices related to government interventions exhibit substantial impacts on the realized volatility, indicating a relative resilience of the Tunisian stock market amidst the challenges posed by COVID-19. However, the application of the bias-corrected wavelet analysis yields more subtle outcomes in terms of the correlations of both measures of volatility to the same metrics. Our econometric implications bear on the application of such a technique, as well as on the use of the realized volatility as an accurate measure of the “true” value of volatility. Nevertheless, the measures and actions undertaken by the authorities do not exclude fear and insecurity from investors due to another virus or any other crisis. The positive and long-term impact on the volatility of US equity market uncertainty, VIX, economic policy uncertainty (EPU), and the infectious disease EMV tracker (IDEMV) is obvious through the autoregressive distributed lag model (ARDL). A potential vulnerability of the Tunisian stock market to future shocks is not excluded. Government and stock market authorities should grapple with economic and financial fallout and always instill investor confidence. Importantly, our results put mechanisms such as overreaction to public news and (in)efficient use of information under test. Questioning the accuracy of announcements is then recommended.

51695 Minimizing the environmental impact of print journals in dermatology

51695 Minimizing the environmental impact of print journals in dermatology

Information Gathering for Online News Publications: A Multidimensional Issue for Media Professionals and Educators

The purpose of this study is to reveal some current issues concerning the gap between the education and practice of Romanian online news reporting, especially regarding the activity of information gathering, critical event reporting and fulfilling the role of exposing reality in an objective manner. Several observations from previous studies showed a discrepancy between the educational standards of journalism schools in the matter of news documentation and the reality on the ground. We tried to see what skills the faculties of future journalists must train in order to ensure a return of the media to a more critical attitude, necessary for a democratic society, starting from the qualitative analysis of several local and national media platforms in Romania. Our main interest was the degree to which reporters that publish news on online media platforms use filed documentation, interviews and other news gathering techniques in their activity, compared to simply publish institutional PR briefings or third-part articles. The conclusions of our research are that the vast majority of news is based solely on curated information and lacks not only a critical approach, but also the personal input of reporters.

Forecasting Stock Market Performance: An Ensemble Learning-Based Approach

There is tremendous value in the ability to predict stock market trends and outcomes. The public sentiment surrounding a stock is unquestionably a vital factor contributing to the rise or fall of a stock price. This paper aims to detail how data from public sentiment can be integrated into traditional stock analyses and how these analyses can then be used to make predictions of stock price trends. Headlines from seven news publications and conversations from Yahoo! Finance’s conversations forum were processed by the Valence Aware Dictionary and sEntiment Reasoner (VADER) natural language processing package to determine numerical polarities that represent a positive, negative, or neutral public sentiment around a stock ticker. The resulting polarities were paired with popular stock-table metrics to create a dataset for a Logistic Regression machine learning model. The model was trained on approximately 4400 major stocks to determine a binary “Buy” (1) or “Not Buy” (0) recommendation for each stock. We present approaches for a variety of machine learning models – Logistic Regression, Random Forest Classifiers, and Extreme Gradient Boosting models and do a statistical comparison on the balanced accuracies of each machine learning model. This paper aims to detail our understanding of how one can leverage the public sentiment of a stock to gauge its future value.

Understanding Business Model of Digital Journalism in Indonesia

The transformation in the media landscape in Indonesia is underscored by the emergence of digital-centric media entities and a new mode of news production distinctly divergent from traditional journalism. The purpose of this study is to examine the evolving business model of digital journalism, a sector experiencing rapid growth following the advent of the internet, social media, and the consolidation of online journalism since 2010. Employing a qualitative-descriptive method, this research is designed to capture the digital journalism business model in Indonesia's post-traditional print and broadcast media systems. From 2021 to 2023, the authors meticulously observed the contents and management of selected news media outlets, conducted semi-structured interviews, and gathered pertinent documents. The key question of this study centers on the nature of the business model underpinning journalism on digital platforms and its repercussions for the interest of news production and publication. The study reveals that business models for digital news media remain unstandardized, and the ecosystem of platform-based journalism has yet to achieve a healthy climate. From the perspective of digital news consumption, the prevailing culture of gratuitous (free) news culture and the concern of keeping trusted news media pose significant obstacles for news corporations in sustaining their operations.

Silenced Voices in Portuguese Public TV News: An Intersectional Analysis of the Representation of Women with Disabilities in RTP’s Telejornal

(1) Background: Studies on women with disabilities (WwD) are rare and tend to reveal certain representation patterns. This study aims to understand how and to what extent WwD and chronic diseases are deemed newsworthy in the Portuguese public news TV station RTP1 primetime news program. Feminist disability, standpoint, agenda-setting, and framing theories are used alongside the concept of intersectionality. (2) Methods: A mixed-method approach is adopted, combining quantitative content analysis of all broadcasted news in January 2020 (n = 704), and qualitative discourse analysis of news items on PwD (n = 5). (3) Results: The results reveal that disability is a reduced issue in Telejornal’s agenda. PwD, in general, are often portrayed in secondary roles and without a voice. The protagonists of news stories about disability or persons with disabilities are predominantly women without disabilities, occupying traditional roles as caregivers (mothers, nurses), while men are more often portrayed as public agents. Discourse analysis deepens understanding by uncovering the prevalence of negative news values and a problem-centred framing, often associated with negativity, rather than presenting solutions. (4) Conclusions: Consequently, WwD were found to be deprived of news representation with a more positive or ‘normal’ focus and an intersectional approach reveals a lack of inclusion, with the few existing news tending to focus on exclusion issues, portraying only white Portuguese women. This study underscores the urgent need for a more equitable approach in media representation, recognising the diversity and positive contributions of WwD to promote an inclusive narrative.

Quantitative Changes in the Publishing of Lithuanian Scientific Journals in 2015–2022: Public and Private Sectors’ Adaptation to the Changing Conditions

The article analyses the quantitative changes in the Lithuanian scientific journal publishing in 2015-2022. The study reveals the problems of public bibliographic accounting and terminology in order to comprehensively analyse the public and private sector published print and electronic scientific journals. The gaps and uncertainties identified allow for the improvement of official publication records in the future, avoiding ambiguities in the assessment of the publication volumes of scientific journals. Science policy makers and implementers should take the initiative to standardise the definition of scientific journals, as this is the only way to achieve a clearer approach to this form of science communication. Although the annual volume of scientific journal publications has shrunk by 18.1% (from 265 (2015) to 217 (2022) publications), the decline itself has not been sharp. The decline has been stabilised by business involvement, changes in the evaluation of colleges, and the transformation of professional journals, book series and conference proceedings into scientific journals. Flexibility has been provided by electronic publishing instruments, which have reduced the dependence on external financial sources. The public publishing sector has tended to reduce the number of scientific journals in all scientific fields. This was mainly due to the reorganisation of the higher education network, the qualitative requirements for scientific articles and the reorganisation of scientific collectives. The publishing business has seen the greatest potential in the fields of technology and the medical and health sciences, and as a result, the number of journals published in these fields has not declined and continues to grow. Despite the private sector's growth trends, 50% of journals have 30 or more years of publication. This forms the core of Lithuanian scientific journals, which public sector publishers are striving to preserve and integrate into the international scientific environment.

A Tech Entrepreneur Turns Local News Publisher

A Tech Entrepreneur Turns Local News Publisher

Pelatihan Pemanfaatan Platform Facebook Sebagai Media Promosi pada Kelompok Tani Lele Sumber Jaya Tambun Selatan

Social media has become a very effective and efficient tool to promote products and services, including in the agricultural sector. The training was held at the Sumber Jaya Catfish Farmers Group Perum Griya Asri I Tambun Selatan District, Bekasi Regency and aimed to improve the ability of its members to utilize the Facebook platform as an effective and efficient promotional media. The methods used in this training include presentations, direct practice, and group discussions. Training materials include making accounts and Facebook pages, promotional content strategies, as well as interaction management techniques with customers. The results of this training showed an increase in the knowledge and skills of participants in using Facebook to promote catfish production in the Sumber Jaya catfish farmer group. Participants are expected to be able to create and manage Facebook pages, as well as design interesting content to increase visibility and sales. This training is expected to be a model for other farmer groups in utilizing social media to expand the market and improve the welfare of group members. The activities of implementing community service with the target of the purpose of the Community Service (PKM) implementing team to make field observations are to directly observe the location of the target area. The outputs to be achieved are: Publications in Print or Electronic Scientific Journal Articles in accredited national journals, articles in national journals are not accredited and local, articles in print or electronic media, both national and local, implementation documentation with photos of activities, and improvement the knowledge and skills of participants in using Facebook to promote catfish production in the Sumber Jaya catfish farmer group.

Framing of Russia-Ukraine War: Comparisons of Global News

This study examined the news framing of the Russia-Ukraine war in global news media outlets. Using the framing theory, a literature review of prominent news frames covering wars and conflicts was drawn. Two hypotheses were set for the major news frames more than others and the relationship between the emphasis on the frames and news organizations’ national interests. Based on predicted framing themes, news frames were obtained from topic modeling with 48,364 news stories. An analysis with a verification sample was implemented to compare the topics with news publishers’ national interests. Five news frames from topic modeling were found: humanism, politics, peace, war, and the economy. In the coverage of the news about the war by global news organizations, the humanism and peace frames were related to Ukraine, whereas the war frame was associated with Russia. The relationship between national interests and pronounced news frames found that European news emphasized humanism more than other regions. The national interest of news outlets was reflected in the news of the war. Study implications and suggestions in terms of news framing and national interests were discussed.