Journalism Research Articles

To Disclose or Not to Disclose: A Comprehensive Analysis Into the Article Transparency of News Websites

An increasing part of the public is distrustful toward journalism. Transparency has been advocated to counter this trend. Therefore, the question arises to what extent news outlets have implemented transparency. General content analyses of the implementation of transparency routines are scarce. We try to add to the literature by conducting a generic quantitative content analysis on the prevalence of a diverse set of transparency routines over multiple news outlets. We also assessed whether transparency not only differs between outlets but also within outlets; namely, between hard vs. soft news section items. We hypothesized that digital-native, public, and quality news outlets and hard news section items would be relatively more transparent. After analyzing 27,096 news items from six news outlets, we find that the outlets differed in the extent to which and areas in which they had implemented transparency: (a) The digital-native news outlet was more transparent than legacy news outlets, except for author transparency, (b) the public news outlet was more transparent in terms of news updates and source use compared to commercial news outlets, but less so in terms of authors and production processes, (c) no substantial systematic differences were found in the extent of transparency implementation between quality and popular news outlets, and (d) hard news section items were only more transparent in source use than in soft news section items. As being one of the only generic quantitative content analyses, this study has contributed to our understanding of the different patterns across news outlets in transparency implementation.

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.

GANs in the Panorama of Synthetic Data Generation Methods

This article focuses on the creation and evaluation of synthetic data to address the challenges of imbalanced datasets in machine learning (ML) applications, using fake news detection as a case study. We conducted a thorough literature review on generative adversarial networks (GANs) for tabular data, synthetic data generation methods, and synthetic data quality assessment. By augmenting a public news dataset with synthetic data generated by different GAN architectures, we demonstrate the potential of synthetic data to improve ML models’ performance in fake news detection. Our results show a significant improvement in classification performance, especially in the underrepresented class. We also modify and extend a data usage approach to evaluate the quality of synthetic data and investigate the relationship between synthetic data quality and data augmentation performance in classification tasks. We found a positive correlation between synthetic data quality and performance in the underrepresented class, highlighting the importance of high-quality synthetic data for effective data augmentation.

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.

Analysing Exit Poll Outcomes and Their Accuracy Across Indian State Assembly Elections Through Descriptive Statistics

The period between polling and the final count of votes is one of the most suspenseful phenomena. Parties eagerly await results after months of campaigning and canvassing for votes. During this period, one often comes across news publications predicting the final results and seat tallies of the election through various surveys conducted by their survey teams on a sample of the electoral population. These surveys are published as exit polls, aiming to give the public and parties an idea of what the end outcomes of elections would look like. Historically, there are multiple examples of the exit polls getting it wrong. Who could forget the great headline of ‘Dewey defeats Truman’ published in the Chicago Daily Tribune in 1948? Even in the Indian general elections of 2004 and 2014, exit poll predictions failed to gauge the direction of public support. This research study investigates a specific period of exit polls and uses descriptive statistics to decode the accuracy of these polls in predicting the final outcomes of elections and the seat projections. Using the elections from the beginning of 2020 till the end of 2022, this study analyses all the State Assembly elections held in this time frame, by comparing the exit polls forecast by four different agencies (Poll of Polls, C-Voter, AXIS and Politique Marquer) in each of those elections and analysing them with the final results of the election by testing the accuracy of these polls through the means of descriptive statistics.

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.

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.

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.

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.

Investigation of the Content Offered to the Public Regarding Noise Pollution in Online News

Background/Objectives: Noise remains an under-discussed type of environmental pollutant, which exerts a wide range of adverse health effects, both auditory and non-auditory. Ensuring that the public has ready access to useful health information online about noise exposure is important. In this regard, evaluating the content of public news articles regarding noise pollution is vital. Although examining the content available to the public on this topic is significant, there is still a dearth of information on this topic. Consequently, this study aimed to analyze the content on Google News regarding noise pollution. Methods: Google News was searched using the term “noise pollution,” and 100 articles were shortlisted for further coding. Qualitative coding was based on the news source, the type of domain extension, and whether a video was included in the article. The articles were quantitatively coded based on their content and compared for length. Results: The three most common content areas were as follows: types of noise pollution (73%), sources of noise (63%), and responses by administrative offices (59%). Only 17% of the news articles alluded to health issues linked to noise pollution, such as sleep disturbances, anxiety, high blood pressure, and effects on children’s auditory skills, that is speech development, concentration, and memory retention. Scientific research findings related to noise pollution were incorporated in less than one-third of the articles. The news articles were of comparable length. Conclusion: This study not only contributes to a better understanding of the news provided to the public and how noise pollution issues are discussed in contemporary, mainstream media, but also can inform about public health initiatives. We recommend that scientific/healthcare communities collaborate with online news media to improve the coverage regarding—and underscore the scientific evidence of—the deleterious consequences of noise on human and animal health.

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.