Journalism Research Articles

Weakening News Bargaining Codes: How Meta’s Algorithm Manipulation Undermines Publisher Performance and Journalism Support

This study investigated the response of Meta, the parent company of Facebook, after Australia’s News Bargaining Code looked like it might be emulated across the globe. The Code was intended to enhance the financial sustainability of news organizations by mandating that platforms pay for news content they use. Analysis using Facebook’s CrowdTangle found that over a one-year period from mid-2022, Facebook reduced by half the visibility of news content of 15 leading publishers on all continents across the globe. This means that the payment envisaged under such codes would be halved. This decline was regardless of their News Bargaining Code implementation status. Publishers reduced their news on Facebook by about 10 percent over the same period, suggesting a strategic shift by news publishers away from reliance on Facebook. These findings demonstrate that platforms can manipulate their recommendation algorithms to undermine the objectives of News Bargaining Codes without directly violating the codes, thereby reducing the codes’ effectiveness as a tool for supporting journalism. Importantly, these findings underscore the need for alternative approaches to support journalism that are less susceptible to platform-driven changes in visibility and engagement dynamics.

Exploring library service preferences: A study of information source usage in engineering colleges

This study investigates the perception and preferences of library services and information sources among patrons at engineering colleges, specifically focusing on the Jaypee Institute of Information Technology (JIIT), Noida. Utilizing a quantitative approach, the research employs a survey method, collecting data from a sample of 382 respondents, including undergraduate students, postgraduate students, research scholars, and faculty members. The survey assesses satisfaction levels across various resources, including print books, e-books, print journals, e-journals/databases, and multimedia resources, with a particular emphasis on AI-based library services. Results indicate that faculty members exhibit the highest satisfaction with library services, particularly those enhanced by artificial intelligence, while undergraduate students show the least satisfaction. The findings highlight the importance of understanding user preferences to enhance library offerings, optimize resource utilization, and improve overall user experience. This research contributes valuable insights into the evolving needs of engineering college patrons in a digital age and offers recommendations for library management to better serve its diverse user base, particularly through the integration of AI-driven services.

Исследование влияния новостной информации об инвестициях и проектах на параметры реакции фондового рынка (на примере сегмента «Металлы и добыча» Московской биржи)

This study examines the reaction of the Russian stock market to the publication of news about companies in the metals and mining segment of the Moscow stock exchange. The article describes the methodology used to collect and analyze a database of news reports, including a modified cumulative abnormal return (CAR) method to register abnormal returns related to news publications. The study also explores the search for parameters such as the beginning, end, duration, and profitability of news using algorithms based on the CAR and normalized abnormal volume (NAV) methods. The results of this analysis are presented in the form of calculated parameters for the beginning, end, duration of the reaction, and CAR, categorized by stock prices and trading volumes.Next, the results obtained from each method are compared. The rationale for the differences in parameter estimates is discussed, as well as the application of these results to improve IR management in companies.

اتجاهات الدراسات الإعلامية العربية في مجال الذكاء الاصطناعي- دراسة تحليلية نقدية

This study analyzes trends in Arab literature that address the use of artificial intelligence (AI) technology in mass media. The research relies on a meta-analysis, or secondary analysis, and incorporates both quantitative and qualitative approaches. The study examines all Arab media studies conducted in this field between 2020 and 2024, which totals 66 studies. One important finding identified by this study is print and electronic journalism were the most frequently examined areas, which was followed by digital media. Studies investigating the current and future uses of artificial intelligence technologies in mass media, as well as the effects and problems arising from their integration, were the most prevalent. Meanwhile, other studies explored the employment and future of AI applications in mass media institutions from the perspective of elites.

How ChatGPT Changed the Media’s Narratives on AI: A Semi-automated Narrative Analysis Through Frame Semantics

We perform a mixed-method frame semantics-based analysis on a dataset of more than 49,000 sentences collected from 5846 news articles that mention AI. The dataset covers the twelve-month period centred around the launch of OpenAI’s chatbot ChatGPT and is collected from the most visited open-access English-language news publishers. Our findings indicate that during the six months succeeding the launch, media attention rose tenfold—from already historically high levels. During this period, discourse has become increasingly centred around experts and political leaders, and AI has become more closely associated with dangers and risks. A deeper review of the data also suggests a qualitative shift in the types of threat AI is thought to represent, as well as the anthropomorphic qualities ascribed to it.

The impact of smart speakers and podcasts on news media consumption

ABSTRACT The increasing diffusion of smart speakers results in substantial challenges for news publishers. Voice-based formats – especially news podcasts – gain significant importance via smart speakers, raising the question of displacement effects on other news media. We investigate the interplay of smart speakers and podcasts with news consumption data from 1,528 respondents collected in Germany. We control for selection effects using propensity score matching and find (I) a higher usage of news podcasts as well as (II) higher podcast news consumption intensity, both driven by smart speaker diffusion. Additionally, podcast usage increases the total cross-media news consumption intensity without displacing other media. We provide converging evidence that smart speakers are a relevant complementary news content channel to access podcasts, which subsequently contribute to strengthening news consumption through other media.

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.

Persuading large investors

A regulator who designs a public stress test to avert default of a distressed bank via private investment must account for large investors' private information on the bank's state. We provide conditions for crowding-in (crowding-out) so that the regulator offers an endogenous more (less) informative signal to better-informed investors. We show that crowding-in occurs as long as investors remain responsive to public news and they are sufficiently well informed: the regulator's test perfectly reveals the state as investors' become privately perfectly informed. Investors' value from more precise private signals may come from their effect on the public test's precision.

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.