News Articles Research Articles

The Great Powers Competition and Increasing Entropy in the Local Media Across Africa and Asia

Abstract The current United States National Defense Strategy prioritizes strategic competition with Russia and China, whereas for the previous two decades, the priority was counter-terrorism. This research aims to measure the extent to which competition among great powers is intensifying and identify where such intensification is taking place. To achieve this, we utilize a large, open-source dataset called GDELT 2.0, which provides an archive of global news articles. We calculate the entropy for local press coverage of great powers (e.g., the United States, China, Russia, France, and India) across 96 African and Asian nations to measure static competition levels. Additionally, we identify African and Asian nations where competition levels have changed significantly between 2015 and 2020. Our findings show that seven countries are experiencing increased competition among great powers, whereas seven other countries experience increased domination by one great power. When we aggregate the data geographically, we find that Southeast Asia is the only subregion where there is a significant decrease in competition. Our results align with the primary concern of the National Defense Strategy: that American influence is decreasing. Specifically, we have identified increased competition across Africa and increased domination across Asia. These trends are due to a relatively higher reporting of Chinese in-country public and private entities between 2015 and 2020.

Political-RAG: using generative AI to extract political information from media content

ABSTRACT In the digital era, media content is crucial for political analysis, providing valuable insights through news articles, social media posts, speeches, and reports. Natural Language Processing (NLP) has transformed Political Information Extraction (IE), automating tasks such as event extraction and sentiment analysis. Traditional NLP methods, while effective, are often task-specific and require specialized expertise. In contrast, Large Language Models (LLMs) powered by Generative Artificial Intelligence (GenAI) offer a more integrated solution. However, domain-specific challenges persist, which led to the development of the Retrieval-Augmented Generation (RAG) framework. RAG enhances LLMs by incorporating external data retrieval, addressing issues related to data availability. To demonstrate RAG’s capabilities, we introduce the Political-RAG system, designed to extract political event information from media content, including Twitter data and news articles. Initially developed for event extraction, the Political-RAG system lays the foundation for developing various complex Political IE tasks. These include detecting hate speech, analyzing conflicts, assessing political bias, and evaluating social trends, sentiment, and opinions.

Searching for Justice: Indigenous Self-Determination over the Landfill Search as a Matter of Justice for MMIWG2S

“Search the Landfill” is an Indigenous-led movement for justice for missing and murdered Indigenous women, girls, and Two-Spirit people (MMIWG2S) which originated through Indigenous resistance to police racism in Winnipeg, Canada. The movement calls upon all levels of government to support and execute the search of the Prairie Green Landfill, just north of the City of Winnipeg, to recover the bodies of at least two of four First Nations women who were murdered in early 2022 at the hands of a white male serial killer. Through an analysis of the news articles, press releases, and reports, this paper explores the way in which Indigenous leaders resisted and rewrote the discourses that surrounded the search. By at once condemning the Winnipeg Police Service’s refusal to search the landfill and rejecting police authority and control over the decision-making process, Indigenous women who became the leaders of this movement crafted the space to articulate for themselves how and why the search must be done as a matter of Indigenous rights. This paper explores how Indigenous leadership offers glimpses of what a decolonizing approach to justice may look like in cases of MMIWG2S and which, in turn, invites further opportunities to problematize, subvert, and move beyond the Western legal norms and traditions.

Impact of the EduIntegrity Suite on Academic Integrity: A Qualitative Study

The EduIntegrity Suite, a collection of tools designed to promote academic integrity, has garnered significant interest within educational institutions. This study explores the Suite's perceived effectiveness and its influence on various stakeholders through a mixed-methods approach. Semi-structured focus group interviews were conducted with students, educators, and administrators at institutions utilizing the Suite. Student discussions explored their perceptions of the Suite's impact on their learning environment and potential concerns. Educators discussed how the Suite influenced their teaching practices and addressed suspected misconduct. Administrator focus groups provided insights into the decision-making process for implementing the Suite, its effectiveness, and its broader cultural influence. An online component complemented the focus groups, involving a comprehensive review of documents and websites related to the Suite. Company websites and product information for the Suite's individual tools were examined. Academic resources discussing the Suite's functionalities and potential applications were reviewed. News articles and blog posts offering insights from educators, administrators, and students regarding their experiences were explored. The findings highlight the Suite's multifaceted functionalities, including plagiarism detection, grammar and citation checking, originality reports, and remote proctoring services. While students acknowledged the Suite's potential deterrent effect, concerns about workarounds and a shift in focus from learning to detecting cheaters were also voiced. Administrators, however, emphasized the Suite's potential as a tool for promoting a culture of academic integrity, not just detecting plagiarism. Transparency and clear communication emerged as crucial for fostering trust and student acceptance. The EduIntegrity Suite appears to offer a promising approach to academic integrity when implemented strategically and combined with effective pedagogical practices. However, the success hinges on a multi-pronged approach that addresses student concerns, utilizes the Suite's functionalities effectively, and fosters a culture of ethical learning within the institution. To achieve this, institutions can prioritize clear communication about the Suite's purpose and limitations, support and develop staff with the Suites functionality and ethical consideration. Moreover, encourage educational initiatives that promote critical thinking, responsible research practices, and proper citation skills. Collaboration between educators and administrators can leverage the Suite's capabilities to create a more supportive and ethically sound learning environment.

AI-generated journalism: Do the transparency provisions in the AI Act give news readers what they hope for?

Issues linked to the increasing presence of AI-generated content in people’s lives, and the importance of being able to effectively navigate and distinguish such content, are inherently linked to transparency, a notion that our study focuses on by evaluating Art. 50 of the AI Act. This article is a call for action to take the interests of end users into account when specifying AI Act's transparency requirements. It focuses on a specific use case – media organisations producing text with the help of generative AI. We argue that in its current form, Art. 50 leaves many uncertainties and risks doing too little to protect natural persons from manipulation or to empower them to take protective actions. The article combines documental and survey data analysis (based on a sample representative of the Dutch population) to propose concrete policy and regulatory recommendations on the operationalisation of the AI Act’s transparency obligations. Its main objective is to respond to the following question: how to reconcile AI Act’s transparency provisions applicable to digital news articles generated by AI with news readers’ perceptions of manipulation and empowerment?

“An Algorithm for Public Service Media?” Embedding Public Service Values in the News Recommender System on RTBF's Platform

In a rapidly evolving digital landscape, public service media (PSM) face a significant challenge: how to leverage recommendation algorithms to enhance their visibility and attractiveness without sacrificing their core values. This study examines the extent to which the Belgian Radio-Television of the French Community (RTBF) has integrated PSM values into the development of a recommender system for news articles on its platform (rtbf.be). The research highlights a conscientious effort to define and translate PSM values before the algorithm design phase, both inside and outside the organization. However, the study also reveals limitations that hinder the full realization of PSM values within the recommender system. Consequently, these values currently function more as abstract guiding principles and communication tools than tangible technical features. The study emphasizes the challenge of reconciling public service ethos with algorithmic realities and concludes by highlighting the need for further empirical research to bridge this gap in the digital age.

Too many numbers and worse word choice: Why readers find data-driven news articles produced with automation harder to understand

The production of data-driven journalism is becoming increasingly automated, impacting its composition and comprehensibility. Given the importance of data-driven reporting for democratic participation, this study investigates, firstly, how readers evaluate the composition of data-driven articles produced with and without the help of automation and, secondly, how these evaluations affect readers’ perceptions of the articles’ comprehensibility. In an online survey experiment, 3135 online news consumers evaluated 24 articles produced with or without automation using criteria developed in prior research. Our factor analysis reduced those criteria to five categories that matter in readers’ evaluations of the articles’ composition: numeric features, writing style, sentence and paragraph length, descriptive language, and word choice. Our results show, firstly, that although the perception of news stories produced with automation did not differ significantly from that of news stories produced without automation regarding sentence and paragraph length and writing style, the stories produced with automation were evaluated as significantly less comprehensible; and, secondly, that this can be explained partly by readers’ perceptions of some of the other article composition categories, which were rated significantly worse in automated articles. Our findings suggest that using automation to produce data-driven news articles changes their perceived composition in ways that negatively impact comprehensibility. However, this study also suggests how such articles could be made more comprehensible. Specifically, when ‘post-editing’ automated articles, journalists should aim to further reduce the quantity of numbers, better explain words that readers are unlikely to understand and change inappropriate wording.

The Austrian Political Advertisement Scandal: Patterns of “Journalism for Sale”

Mounting concern surrounds the influence of political actors on journalism, especially as media outlets face increasing financial pressures. These circumstances can give rise to instances of media capture, a mutually corrupting relationship between political actors and media organizations. However, empirical evidence substantiating such mechanisms and their consequences remains limited, particularly in the context of Western democracies. This chapter investigates a recent case in which a former Austrian chancellor allegedly colluded with a tabloid newspaper to receive better news coverage in exchange for increased ad placements by government institutions. We employ automated content analysis to investigate political news articles from seventeen prominent Austrian news outlets spanning 2012–2021 ( n = 222,659). Adopting a difference-in-differences approach, we find a substantial increase in media visibility of the former Austrian chancellor within the news outlet that is alleged to have received bribes, as well as a decrease in favorability for challenger candidates. Although this study does not aim to prove or disprove the involvement of specific political actors or media organizations in unethical or illegal activities, it introduces an innovative method for detecting unusual patterns in media reporting. Findings are discussed in the context of current threats to media independence and underscore the crucial need to protect journalistic integrity and ensure unbiased information for the public.

Developing a novel, systematic form of Media Framing Analysis to discursively examine the UK’s Employment and Support Allowance policy

ABSTRACT In 2008, the UK government introduced Employment and Support Allowance (ESA) as a welfare policy for individuals who are unable to work due to sickness or disability. Our research project examined the cultural landscape that has been made ‘available’ for those living with chronic health conditions whilst claiming welfare. To increase the transparency of a Foucauldian-inspired discursive analysis, PRISMA reporting guidelines were incorporated into a Media Framing Analysis. News articles and policy documents were collected as data corpora. This paper outlines how this innovative approach was developed; sharing samples of the analytical findings to illustrate what was achieved through a systematic compiling of a data set. A genealogical approach to health-based welfare shows how temporal and dualistic discourses of illness synonymise ‘health’ with a capability for employment. Methodological considerations are explored; illuminating how a systematic approach to data compilation can improve the transparency, rigour and potential transferability of discursive analyses.

Integrating ChatGPT with Salesforce for Real-Time Market Insights on Accounts

This paper explores the integration of ChatGPT, an AI-driven natural language processing model, with Salesforce to enhance sales teams' access to real-time market information about specific accounts. By integrating ChatGPT with Salesforce through API calls, users can fetch the latest market trends, news articles, stock prices, financial data, legal filings, and acquisitions related to specific accounts directly on the Account detail page. This integration not only improves decision-making and customer engagement but also offers a scalable, automated solution that saves time and enhances data accuracy. The paper includes coding examples, architecture diagrams, and use cases to demonstrate the implementation, and it discusses key performance indicators (KPIs) for assessing its effectiveness in real-world applications. Keywords: Salesforce, ChatGPT, AI Integration, Real-Time Data, Account Insights, API, Market Trends, CRM, Natural Language Processing, Automation

A Novel NLP-based Stock Market Price Prediction and Risk Analysis Framework

Stock market prediction is an interesting and complex problem that has recently been in the limelight, thanks to the significant accuracy achieved by deep learning models. However, a complete platform with prediction and risk analysis ability is unavailable. In the current work, we present a novel framework for investment analysis designed to create ease for investors and provide a confidence measure along with the stock price to depict the risk involved in investing in stocks of a particular company. The model integrates two different approaches successfully to improve accuracy significantly. The model inputs two sources – a stock price dataset depicting the original scores as numerals and textual data extracted from Reddit news articles. The traditional problem of stock price prediction is dealt with using LSTMs on individual stock prices. At the same time, the confidence is represented by a risk value calculated intelligently using XGBoost and LSTM output. We have deployed natural language processing techniques for performing sentiment and subjectivity analyses, which are then used to extract features for further investigation in the study. The results show that an accuracy of 94% for stock trend prediction can be achieved using PCA as the feature extractor with tuned parameters for XGBoost and around 76% accuracy for stock price prediction with a tuned LSTM. It removes the hassle for investors to research the project or company they want to invest in and provides all relevant analysis and data.

Detection of news written by the ChatGPT through authorship attribution performed by a bidirectional LSTM model

The large language based-model chatbot ChatGPT gained a lot of popularity since its launch and has been used in a wide range of situations. This research centers around a particular situation, when the ChatGPT is used to produce news that will be consumed by the population, causing the facilitation in the production of fake news, spread of misinformation and lack of trust in news sources. Aware of these problems, this research aims to build an artificial intelligence model capable of performing authorship attribution on news articles, identifying the ones written by the ChatGPT. To achieve this goal, a dataset containing equal amounts of human and ChatGPT written news was assembled and different natural processing language techniques were used to extract features from it that were used to train, validate and test three models built with different techniques. The best performance was produced by the Bidirectional Long Short Term Memory (LSTM) Neural Network model, achieving 91.57% accuracy when tested against the data from the testing set.

뉴스 기사 빅데이터 분석을 통한 북한이탈주민의 사회적 이슈와 언어 문제 탐색: 2019-2024

Objectives The purpose of this study is to explore social issues and language problems that appear in news articles related to North Korean defectors and to suggest their implications. Methods 561 news articles related to North Korean defectors covered by major domestic newspapers and media companies from 2019 to June 2024 were collected. For analysis tools, language network analysis and topic modeling techniques were used to analyze major keywords and topics and visualize them. In addition, based on the analysis results, social issues and language problems of North Korean defectors were explored, and Korean language education implications were presented. Results he research results are as follows. First, the amount of news coverage has been increasing significantly over the past three years, and political articles have shown an overwhelmingly high proportion. Second, the main keywords are ‘government, support, human rights, protection, and settlement’. The main topics were protection of human rights of North Korean defectors, increasing number of North Korean defectors among elites and the 2030 generation, and complaints of difficulties due to mental and economic discrimination and prejudice in the process of settling in Korea. The content of the government's integrated support policy accounted for a high proportion. Third, it was found that North Korean defectors were struggling with the language difference between North and South Korea, and Korean language education focusing on pronunciation, accent, and vocabulary in each area was necessary for them. Conclusions The main topic of the news article was the integrated support for the protection of human rights and stable settlement of North Korean defectors. However, although North Korean defectors complain of ‘difficulty’ due to economic, psychological, emotional, and linguistic differences, these related topics are hardly covered in the media. Therefore, this paper suggested implications for Korean language education research for North Korean defectors' language adaptation.

On the Way to BRICS+ Digital Sovereignty: Opportunities and Challenges of a New Era

A new era for BRICS has begun with the desire of new countries to join BRICS. This expansion, the BRICS+, poses several challenges and opportunities for the renewed alliance, particularly concerning the digital sovereignty of the countries. On the one hand, the leading five BRICS nations have the potential to achieve digital sovereignty, earning the moniker “the hawks of digital sovereignty.” On the other hand, expanding BRICS membership to countries with varying levels of digitalization raises issues for the alliance. These include improving national legislation on digital sovereignty and defining actions to foster cooperation within BRICS+. This article aims to design a theoretical legal model for BRICS+ digital sovereignty, outlining its pillars and offering recommendations for achieving digital sovereignty within BRICS+. The comparative legal method, used to analyze regulations in digitalization and digital sovereignty among BRICS+ member countries, ensures a comprehensive understanding of the legal landscape. Retrospective analysis, which studied the development of BRICS+ regulations in these areas, provides a historical context. The systematic method, which examined legal tools and instruments that contribute to achieving digital sovereignty, ensures a thorough exploration. The content analysis allowed for the interpretation of news articles and social media sources related to BRICS+ digital sovereignty, adds a contemporary perspective. The authors conclude that achieving digital sovereignty for BRICS+ is possible and offer several recommendations for collaboration, including developing a BRICS+ digital sovereignty memorandum, launching a BRICS+ regulatory sandbox, and deploying a BRICS+ sovereign cloud. These recommendations can inform BRICS+ policy-making, contribute to the limited literature in this field, and serve as a basis for future research on BRICS+ digital sovereignty.

Public understanding of preprints: How audiences make sense of unreviewed research in the news.

News reporting of preprints became commonplace during the COVID-19 pandemic, yet the extent to which the public understands what preprints are is unclear. We sought to fill this gap by conducting a content analysis of 1702 definitions of the term "preprint" that were generated by the US general population and college students. We found that only about two in five people were able to define preprints in ways that align with scholarly conceptualizations of the term, although participants provided a wide array of "other" definitions of preprints that suggest at least a partial understanding of the term. Providing participants with a definition of preprints in a news article helped improve preprint understanding for the student sample, but not for the general population. Our findings shed light on misperceptions that the public has about preprints, underscoring the importance of better education about the nature of preprint research.

Enhancing social interaction in nursing homes through public tabletop displays: A field study of R2S

The prevalence of social isolation among nursing home residents highlights the need for innovative solutions to enhance their social interactions and wellbeing. Despite extensive efforts over recent decades to develop in-room systems or public applications under caregivers’ supervision, there remains a gap in designing public social technologies that can be integrated into residents’ daily lives. Furthermore, the challenges associated with low adoption and the complexities of evaluating such technologies in public care settings have limited our understanding of their impact on residents' social activities and experiences. To address this, our study introduces R2S, a tabletop display system designed to encourage older adults to view, share, and discuss news articles collaboratively in public care environments. A 6-week field trial was conducted in a Dutch care home to investigate the influence on residents’ daily social activities, R2S's utilization, and residents’ perceived user experiences and social emotions. The results reveal that R2S can generally promote daily social interactions among residents, even altering their long-established social habits. Five usage patterns of social technologies within public care environments were identified to provide insights into designing systems in comparable contexts. The participants reported a highly positive user experience with the system. Although their affective social benefits were not significant, they substantially outweighed the affective costs. These findings not only deepen the understanding of how technology can enhance social interaction in public care settings but also provide insights to inform the design and implementation of related technologies in the future.

The Efficacy Potential of Cyber Security Advice as Presented in News Articles

Abstract Cyber security advice is a broad church: it is thematically expansive, comprising expert texts, user-generated data consumed by individual users via informal learning and much in-between. While there is evidence that cyber security news articles play a role in disseminating cyber security advice, the nature and extent of that role are not clear. We present a corpus of cyber security advice generated from mainstream news articles. The work was driven by two research objectives. The first objective was to ascertain what kind of actionable advice is being disseminated; the second was to explore ways of determining the efficacy potential of news-mediated security advice. The results show an increase in the generation of cyber security news articles, together with increases in vocabulary complexity and reading difficulty. We argue that these could present challenges for vulnerable users. We believe that this corpus and the accompanying analysis have the potential to inform future efforts to quantify and improve the efficacy potential of security advice dissemination.

Improving Access to Hygiene, Sanitation, and Drinking Water in King County and Beyond: Success Factors and Costs

Context: Most major urban areas in the US, including Seattle and King County, have a long-standing lack of public restrooms, handwashing stations, and drinking water, presenting public health risks. Objective: To aid decision-makers in expanding access, we review available information regarding successful hygiene programs in urban settings to identify shared characteristics and costs. Design: We reviewed 10 journal articles, 49 news articles, and 54 pieces of gray literature including reports, white papers, and online resources describing real-world hygiene, sanitation, and drinking water programs in US and global urban settings. We selected programs in 8 cities and applied a thematic analysis to identify shared success factors. We also summarized costs where available. Settings: Calgary (Canada), Denver (Colorado), London (United Kingdom), Los Angeles (California), Portland (Oregon), San Francisco (California), Seattle (Washington), and Vancouver (Canada). Results: Successful programs usually provide frequent cleaning and maintenance, are designed and operated to discourage crime and misuse, leverage existing infrastructure, and include mobile solutions. Cities can expect an initial cost of at least $133 000 per toilet and annual operating costs of at least $100 000 per toilet. Conclusions: By employing proven solutions and bringing them to scale over time, cities can promote health while improving quality of life and facilitating movement through public spaces for all. Costs should be understood in the context of expenses such as sidewalk cleaning and human waste removal that are necessitated by a lack of restrooms.

Media Perspectives on Indonesian Policies against Illegal Online Gambling: A Framing Analysis

Online gambling is illegal in Indonesia, both legally and socially. Nevertheless, it has gained popularity among various segments of society, including teenagers, adults, and even government employees. This study aims to understand the media's perspective on the handling of online gambling in Indonesia. Utilizing Robert Entman's framing analysis theory, the research focuses on four elements: defining the problem, diagnosing the cause, making moral judgments, and providing treatment recommendations. The sample consists of news articles from Kompas.com between June 2023 and July 2024. The findings reveal that Kompas.com emphasizes government actions such as the arrest of gambling operators, online gamblers, and celebrities promoting online gambling as the defined problem. In diagnosing the cause, the reports highlight online gambling as a complex crime involving international networks and cybersecurity issues. The moral judgment presented by Kompas.com underscores the negative impacts of online gambling, press freedom, and societal morality. The treatment recommendations suggested include stringent enforcement and deterrent penalties, as well as international cooperation to eradicate online gambling in Indonesia.

Do News Reports and Google Searches Impact Bitcoin Prices? An Analysis of Granger Causality

Objective: to analyze the Granger causality relationship between the number of Bitcoin news articles, the number of Google searches related to the term "Bitcoin," and the price of the cryptocurrency. The study aims to understand how these variables influence each other using an econometric time series model. Theoretical framework: the paper is based on studies on financial market efficiency, cryptocurrencies, and Granger causality theory. It reviews research that explores the impact of media information and online searches on the price of financial assets, such as Bitcoin, while also discussing Bitcoin’s role as both a speculative asset and a transactional currency. Method: the construction of a time series with weekly data, from 2015 to 2018, covering Bitcoin price, the number of news articles, and Google searches. The Vector Autoregressive (VAR) model was used to conduct the Granger causality test and verify if there are causal relationships between these variables. Results and Discussion: the findings indicate a triple causality relationship among the studied variables. Bitcoin's price influences both the number of news articles and Google searches, and in turn, the articles and searches also influence Bitcoin's price. This suggests that increased interest and media coverage of Bitcoin drive its demand and price. Research implications: the research suggests that Bitcoin's price behavior is influenced not only by traditional economic factors but also by media information and the volume of online searches. This highlights the importance of informational factors in the cryptocurrency market and the influence of digital media on asset pricing. Originality/value: the study proposes integrating data from news articles and Google searches with Bitcoin price behavior, applying Granger causality analysis. Its contribution lies in demonstrating the importance of media in the dissemination and pricing of cryptocurrencies, providing a new perspective for the literature on digital assets and speculative behavior.