News Content Research Articles

The Trend of Media Convergence and its Impact in Journalism and Communication Studies

The trend of media convergence and its impact in the field of journalism and communication studies has become increasingly evident. As information technology rapidly evolves, the boundaries between traditional and new media are becoming blurred. Various media formats are integrating deeply in terms of content, platforms, and channels, bringing unprecedented changes to the realm of news dissemination. The trend of media convergence not only drives innovation in the ways news is communicated but also significantly impacts both the theoretical research and practical applications within journalism and communication studies. In terms of communication methods, media convergence allows news content to be presented in more diversified and personalized ways, meeting the diverse needs of audiences. Cross-platform dissemination and interactive communication are becoming the norm, significantly improving the speed and efficiency of news dissemination. Theoretically, media convergence offers new perspectives and methodologies for journalism and communication studies. The impact of media convergence on news dissemination processes, audience behavior, and communication effects has garnered widespread attention, exploring new laws and characteristics of news dissemination. However, media convergence also presents a series of challenges. Thus, continuous innovation and transformation are needed to adapt to the new communication environment and development needs, promoting the ongoing development and progress of journalism and communication studies.

Public health framing of firearm violence on local television news in Philadelphia, PA, USA: a quantitative content analysis

BackgroundFirearm violence is an intensifying public health problem in the United States. News reports shape the way the public and policy makers understand and respond to health threats, including firearm violence. To better understand how firearm violence is communicated to the public, we aimed to determine the extent to which firearm violence is framed as a public health problem on television news and to measure harmful news content as identified by firearm-injured people.MethodsThis is a quantitative content analysis of Philadelphia local television news stories about firearm violence using a database of 7,497 clips. We compiled a stratified sample of clips aired on two randomly selected days/month from January-June 2021 from the database (n = 192 clips). We created a codebook to measure public health frame elements and to assign a harmful content score for each story and then coded the clips. Characteristics of stories containing episodic frames that focus on single shooting events were compared to clips with thematic frames that include broader social context for violence.ResultsMost clips employed episodic frames (79.2%), presented law enforcement officials as primary narrators (50.5%), and included police imagery (79.2%). A total of 433 firearm-injured people were mentioned, with a mean of 2.8 individuals shot included in each story. Most of the firearm-injured people featured in the clips (67.4%) had no personal information presented apart from age and/or gender. The majority of clips (84.4%) contained at least one harmful content element. The mean harmful content score/clip was 2.6. Public health frame elements, including epidemiologic context, root causes, public health narrators and visuals, and solutions were missing from most clips. Thematic stories contained significantly more public health frame elements and less harmful content compared to episodic stories.ConclusionsLocal television news produces limited public health coverage of firearm violence, and harmful content is common. This reporting likely compounds trauma experienced by firearm-injured people and could impede support for effective public health responses to firearm violence. Journalists should work to minimize harmful news content and adopt a public health approach to reporting on firearm violence.

Local News Reporting and Mass Attitudes on Infrastructure Investment

AbstractA growing body of research documents how shrinking local newsrooms undermine political accountability and local fiscal and policy performance in the United States. We extend this work to examine political impacts from the level of information content in local news, which has been jeopardized by reductions in newsroom staffing. To understand how information content affects public response to news coverage of a local issue, we focus on the case of preventive spending on infrastructure maintenance and repair. Inefficiently low levels of infrastructure investment are often attributed to low public knowledge about the risk of failure events. In a preregistered survey experiment, we test how the level and type of information in a news article affect support for infrastructure investment across two different types of infrastructure risk (repeated nuisance versus catastrophic failure). For both types of risk, we find that more information-rich reporting, whether investigative or event-driven, increases public support for preventive spending and imposes accountability penalties on local leaders who fail to invest in prevention.

Real-Time News Aggregation Using ReactJS: A Case Study and Analysis

Abstract: Real-time news aggregation has become increasingly vital in the digital age, where access to upto-date information is crucial for individuals and organizations alike. This paper presents a comprehensive study on the development of a real-time news application using ReactJS, a popular JavaScript library for building user interfaces. The study explores the significance of real-time updates in news applications, reviews existing literature on news aggregation platforms and ReactJS development, and identifies gaps in the current research landscape. The proposed system leverages ReactJS's flexibility and efficiency to create an intuitive user interface for accessing and interacting with real-time news content. Through integration with external APIs such as News API, the application fetches news articles from various sources and presents them to users in a timely manner.

Analysis of News Source Selection in the Digital Age: A Case Study of Micro-platforms Operated by People’s Daily and Southern Weekly

This study employs content analysis to compare and examine the selection of news sources in the micro-platforms of mainstream media, People’s Daily, and urban media, Southern Weekly. It attempts to interpret the impact of the digitalization process on the content production practices of different news media in China. The research reveals that in the digital era, media attributes and platform regulations are direct factors influencing the selection of news sources in news production. This transformation has significantly altered traditional news content production. Official news sources remain the primary choice for news media in producing content in the digital age. Additionally, grassroots news sources, representing the general public, are gradually incorporated into various types of reporting, demonstrating a trend towards democratization.

A critical discourse analysis of Jayland Walker's demise in digital journalism

This research investigates the shooting incident involving Jayland Walker, an African American citizen, on June 27, 2022, resulting from a traffic violation in Akron, United States, and its subsequent coverage across major online media outlets such as CNN News, NBC News, and The Washington Post. Utilizing qualitative methods and drawing on Van Dijk's (1985) Critical Discourse Analysis (CDA) model, the study focuses on analysing the ideologies embedded in ten online news stories from June 2022 to August 2022. The findings highlight a discernible trend in the representation of Jayland Walker by CNN News and The Washington Post, where he is predominantly portrayed as a victim, with the police depicted negatively through strategic use of lexical elements and metaphors, underscoring the influence of media ownership ideologies in shaping news content. In contrast, NBC News maintains a more neutral stance, attributed to frequent changes in ownership. The research contributes theoretically to CDA, providing a conceptual framework for students engaged in critical discourse analysis studies, and offers insights into the complexities of media representation in incidents involving discrimination and police violence.

Creating Value for the Digital Reader in the Context of Sustainable Journalism

Because of digitalisation, social media platforms usage for news follow-up, the changing news consumption habits of young readers, and the COVID-19 pandemic, traditional journalism has been in a crisis for a while. Social media has become a significant competitor for news organisations in terms of both news consumption and advertising. Using the findings of the news consumption habits survey conducted with 200 participants in the United Kingdom (in London and Cardiff), this study discusses readers' expectations and consumption habits in the context of sustainable journalism. The survey constitutes a part of large-scale research carried out with the financial support of TÜBİTAK. It is revealed that readers heavily prefer digital platforms, including social media, for news follow-up, and this interest increased during the Pandemic. The main expectations of the consumer/reader are reliability, easy access/use, easy-to-follow, personalised/reader-specific content, and less advertising. Among the findings is that young readers prefer social media platforms that are not "news organisations" to follow the news. Reader-based revenues are regarded as the most crucial income for sustainable journalism. Media needs to understand the reader's expectations and produce news content that will create value for them to generate sustainable reader income.

A review of challenges, algorithms and evaluation methods in news recommendation

News reading is an important social activity and to help readers quickly find news articles of their interest, news content providers and aggregators use recommender systems. Such systems are designed to address a variety of challenges. Inspiration for algorithmic design is taken from various domains which has resulted in the creation of an enormous body of literature. Also, different methods are used for evaluation of the recommendation algorithms. In this study, we review these developments and present three major components in news recommendation research. First, we list and categorise the challenges faced while designing news recommender systems. We especially list the different algorithmic designs used for generating personalised and non-personalised recommendations. We discuss the major neural network architectures that are being increasingly used for both collaborative and content-based recommender systems. Next, we list the two major evaluation methods and also list some popular datasets used in evaluation. Finally, we identify the emerging trends in news recommender research. We find that the issues related to fake news, trust and use of personal data for news recommendation are gaining wider attention, and deep learning methods are being increasingly used to address these issues.

Auditing Entertainment Traps on YouTube: How Do Recommendation Algorithms Pull Users Away from News

ABSTRACT Recommendation algorithms that customize information feeds for individuals have raised concerns about exacerbating inequalities in news exposure among citizens. In response to these concerns, we conducted an audit study on YouTube to analyze the algorithmic impact on curating news versus other content topics. We examined over 1.7 million YouTube video recommendations audited in 2019 and developed novel analysis approaches including network analysis and Markov chains. Results show that recommendation algorithms may potentially redirect users away from news content through two influence pathways: (1) the “topical filter bubbles,” wherein entertainment content has a higher probability of being recommended over news content in a self-reinforcing manner; and (2) “algorithmic redirection,” wherein the probability of entertainment videos being recommended after a news video is much higher than that for the opposite. Overall, YouTube recommendation algorithms have a higher probability of recommending entertainment videos than news. The findings imply essential biases in algorithmic recommendations on digital platforms beyond amplifying users’ preferences.

Which storytelling people prefer? Mapping news topic and news engagement in social media

Nations engage in a nuanced competition to achieve soft power through social media on a global scale. Exploring the intricate dynamics, research endeavors focus on unraveling the mechanisms by which national media endeavors not only captivate a diverse international audience but also amplify their influence through carefully crafted narratives. However, extant research falls short in both theoretical and empirical examinations of the intricate interplay between news content and news engagement. This research examines in depth the news content strategy employed by China's national media for global communication, focusing on 6632 YouTube videos of CGTN (China Global Television Network) from May 2021 to August 2023. Specifically, utilizing BERTopic machine learning techniques, we identified 16 distinct news topics and associated with news engagement metrics (e.g., number of views, likes, and comments). Our results reveal a pattern of selective engagement and avoidance by international audiences, particularly in their significant interest in geopolitical and technological topics, while showing less interest in Chinese cultural and social news. This study offers valuable insights into the effectiveness of national media's digital storytelling strategy, shedding light on audience preferences and areas for potential recalibration in content dissemination.

NewsGuesser: Using Curiosity to Reduce Selective Exposure

Selective exposure has long been a concern of HCI researchers as it can lead to ideological polarization and distrust in society. Efforts have tried to reduce selective exposure online by serving diversified news content, but their effectiveness has been limited by users' lack of motivation to engage with the diverse content offered. To address this, we design the NewsGuesser system, which leverages the insight that curiosity can prompt motivation and engagement, by asking readers to guess the source of their news. In interviews with 40 participants, balanced for partisan affiliation, we use NewsGuesser as a probe tool to explore how guessing affects their perceptions of selective exposure. Participants struggled with the guessing game, which revealed a misalignment between users' expectations of different news sources and reality. Faced with the visualizations of the (often inaccurate) guessing results, participants were able to reflect on their own biases and selective exposure. In a number of cases, the guessing process changed participants' impressions of news organizations and some expressed an interest in engaging with more diverse news sources. While many also found the guessing game frustrating, the system and interview results suggest a number of new directions for designing social media and news media platforms.

Enhancing the Identification of False News using Machine Learning Algorithms: A Comparative Study

In today's digital world filled with information overload, preventing the rampant spread of fake news has become an urgent task. Discover the Fake News Prediction System (FNPS), which uses advanced machine learning and technology to provide innovative solutions and powerful methodologies. Natural language processing methods. FNPS uses sophisticated feature engineering from diverse, curated datasets to identify underlying patterns in fraudulent content and significantly improves the ability to recognize authenticity. FNPS achieves outstanding performance using a combination of classifiers combining TF-IDF vectorization, deep learning architecture, and sentiment analysis, demonstrating its ability to accurately predict the legitimacy of news articles. Beyond simple forecasting, FNPS provides an intuitive user interface for evaluating news content in real time. This not only increases accessibility but also promotes media literacy and responsible consumption of information. Provides additional information and promotes robust public discourse. FNPS essentially demonstrates the revolutionary potential of advanced technology in ongoing combat. This will further the important public goal of ensuring the reliability and integrity of information in the digital age.

Emerging Trends in Digital Journalism: A Comprehensive Review

Digital journalism has undergone significant transformations in recent years driven by advancements in technology and shifts in audience preferences. This research paper explores the latest trends shaping the landscape of digital journalism examining their impact on news production, distribution and consumption. Drawing upon an extensive review of literature, case studies, and industry reports, this paper identifies and analyzes key trends such as data journalism, immersive storytelling, mobile journalism, audience engagement strategies, and the rise of AI-driven content creation. Furthermore, it examines the challenges and opportunities presented by these trends for journalists, news organizations, and society as a whole. By understanding and adapting to these evolving trends, journalists can effectively navigate the dynamic digital media environment and continue to deliver high-quality, relevant, and engaging news content.

American Police Brutality in Black and White: Fox News Website Coverage of the Killings of George Floyd and Tyre Nichols

The police-perpetrated killings of George Floyd in 2020 and Tyre Nichols in 2023, respectively, provide a unique opportunity for comparison of news content. Both incidents were recorded on video and appear to be characterized by unwarranted aggression and brutality. A key difference worth exploring is the race of the perpetrator(s). In the case of Floyd, the killer, Derek Chauvin, was white, while Nichols’ killers were black. The purpose of the present research was to comparatively examine how the Fox News website framed these two police-perpetrated killings. Quantitative content analysis was used to examine Fox News website coverage of the murders of Floyd and Nichols during the one-month periods following the publicity of both incidents. Findings were consistent with researcher expectations that Fox News would be more sympathetic to Nichols than Floyd, and more critical of the black police officers who killed Nichols than the white officer who killed Chauvin. In all, eight out of the study’s ten sub-hypotheses were supported.

Social and economic problems of regional TV news in the conditions of the Russian-Ukrainian War (on the example of the TV channel “3-Studio” in Ivano-Frankivsk)

The article is devoted to researching the news content of regional media during the full-scale war between Russia and Ukraine. The place of local information resources in the modern communication paradigm, the implementation of which is based on segmented information interests, digital technologies and the principle of global journalism in regional, national and world media discourses, is defined.
 The transformation of the activity of information resources in Ukraine, caused by full-scale Russian aggression, is outlined, and the change in the behavior of the media audience regarding information consumption is substantiated, which led to the growth of Ukrainians’ trust in regional media. The modification of the regional media discourse in the focus of the local prism of the perception of events of national significance is meaningful.
 The importance of researching the news segment of regional television companies in the focus of the socio-economic section is clarified. It is emphasized that the materials of this problematic and thematic spectrum form a general picture of the impact of the war on the region and society in general. On the example of the regional Ivano-Frankivsk television and radio company “3-Studio”, the dominance of socio-economic materials in the news television discourse of the region, their relevance during the war and the influence on public opinion is substantiated.
 The transformation of the quantitative representation of specialized news content on regional television during the two years of the war is traced, the main economic and social narratives of regional news are identified, the reasons for the change in vectors in the presentation of socio-economic issues in the region are analyzed. The role of the regional Ivano-Frankivsk television and radio company “3-Studio” in the context of forming a holistic picture of reality, developing public dialogue and understanding the complexities associated with military aggression is proved.

Analisis Video Pembelajaran Materi Teks Berita pada Kanal Youtube untuk Pembelajaran Bahasa Indonesia

This study aims to find out what are the learning videos of news text material that are already on YouTube based on the characteristics, specifications, and the advantages and disadvantages of the videos. This study uses a type of qualitative descriptive research. The data analysis technique uses the data analysis technique Miles and Huberman. This research gets results with videos that are generally 5-10 minutes long, in general the videos are in accordance with KD, indicators and learning objectives of news texts. Apart from that, video specifications are obtained with an aspect ratio of 16:9, resolution of 720 (HD), and a frame rate of 30fps. There are 40 learning videos on YouTube analyzed based on Miles and Huberman according to research criteria and a review of learning videos, 22 learning videos for news texts are found to be appropriate. Video material regarding identification of news elements and identification of news elements and concluding news content.

EKONOMI POLITIK PERLINDUNGAN HAK KEKAYAAN INTELEKTUAL: STUDI KASUS KEBIJAKAN PUBLISHER RIGHTS AUSTRALIA TERHADAP GOOGLE DAN FACEBOOK

Australian news media publishers are facing serious challenges as Google and Facebook emerge as digital platforms in the Australian media industry. They have transformed into the main pillars of media industry as they monopolize distribution and monetization of news content as well as the digital advertising market, thus dominate Australia’s digital market share. Their power and dominance in controlling the flow of information and digital advertising have drawn attention of the Australian government. In response, the Australian government enacted the publisher rights policy through adoption of the News Media and Digital Platforms Mandatory Bargaining Code (NMBC). Google and Facebook reacted strongly against the NMBC and threatened to close the services in Australia. This research aims to explain the reasons behind Australia’s enactment of the publisher rights policy against Google and Facebook. Using the analytical descriptive research methods combined with theory of regulatory mercantilism, concept of digital sovereignty, and concept of intellectual property rights, this study argues that enactment of the publisher rights policy is due to the imbalance of bargaining power between the Australian news media publishers and Google and Facebook. In addition, this policy was enacted due to the reduction in traditional revenue received by the traditional news media publishers as a result of declining advertising revenue, content purchases, and subscriptions that pose a threat to sustainability of Australia’s news media industry. Lastly, the adoption of the publisher rights policy is due to the centralized media power of Google and Facebook which endangers diversity of Australian news media.

Сайт образовательной организации как инструмент создания образа виртуального автора (на материале заголовков новостного контента официального сайта вуза)

Internet sites are tools of communication that require a comprehensive study of all aspects, e.g., that of the virtual author who carries out communicative intentions through Internet discourse. A virtual author is a collective figure represented in the discourse of a company website. The authors modelled a virtual author as a collective representative of an educational institution that promotes a positive image of this institution while informing about its agenda and activities. The analysis involved 50 news headlines published on the official website of the Kuzbass Institute of the Federal Penitentiary Service of Russia in 2022-2023. The methods of content and intent analyses made it possible to identify the units of the propositional frame, such as a) participants of events, i.e., subjects; b) predicates that characterize the participants and indicate the type of the event; c) chronotope. The virtual author of the news content appeared to be the Institute as a collective person that expresses their communicative intentions by informing the audience, thus shaping a positive image. As a subject, the virtual author reported news about the object, i.e., the Institution, and implemented the communicative intentions, i.e., to inform and promote a positive image. Each subject was part of the university staff: We are Together; We are a Team; We Participate in Important Events; We have Socially Significant Partners; We are the Best; We Support the City, the Region, and the Country, etc. The communicative intention to create an image of a successful university determined the choice of lexical units used in the propositional frame to explicate the objective mode and implicate the subjective one.

The impact and management of internet-based public opinion dissemination during emergencies: A case study of Baidu News during the first wave of coronavirus disease 2019 (COVID-19).

The coronavirus disease 2019 (COVID-19) public health emergency has had a huge impact worldwide. We analyzed news headlines and keywords from the initial period of COVID-19, and explored the dissemination timeline of news related to the epidemic, and the impact of Internet-based media on the public using lifecycle theory and agenda-setting theory. We aimed to explore the impact of Baidu news headlines on public attention during the first wave of COVID-19, as well as the management mechanism of regulatory departments for social public opinion. We searched Baidu News using the keywords "Novel Coronavirus" and "COVID-19" from 8 January to 21 February 2020, a total of 45 days, and used Python V3.6 to extract news samples during the first wave of the epidemic. We used text analysis software to structurally process captured news topics and content summaries, applied VOSviewer V6.19 and Ucinet V6.0 to examine key aspects of the data. We analyzed the impact of Baidu News headlines on social opinion during the first wave of COVID-19 in the budding, spread, and outbreak stage of the information lifecycle. From clustering visualization and social network analysis perspectives, we explored the characteristics of Baidu News during the initial stage of the COVID-19. The results indicated that agenda-setting coverage through online media helped to mitigate the negative impact of COVID-19. The findings revealed that news reporting generated a high level of public attention toward a specific emergency event. The public requires accurate and objective information on the progress of COVID-19 through Baidu News headlines to inform their planning for the epidemic. Meanwhile, government can enhance the management mechanism of news dissemination, correct false and inaccurate news, and guide public opinion in a positive direction. In addition, timely official announcements on the progress of the COVID-19 outbreak and responses to matters of public concern can help calm tensions and maintain social stability.

Bridging the Gap

Effective organization and retrieval of news content are heavily reliant on accurate news classification. While the mountainous research has been conducted in resourceful languages like English and Chinese, the researches on under-resourced languages like the Kurdish language are severely lacking. To address this challenge, we introduce a hybrid approach called RFO-CNN in this paper. The proposed method combines an improved version of red fox optimization algorithm (RFO) and convolutional neural network (CNN) for finetuning CNN’s parameters. Our model’s efficacy was tested on two widely used Kurdish news datasets, KNDH and KDC-4007, both of which contain news articles classified into various categories. We compared the performance of RFO-CNN to other cutting-edge deep learning models such as bidirectional long short-term memory networks and bidirectional encoder representations from transformers (BERT) transformers, as well as classical machine learning approaches such as multinomial naive bayes, support vector machine, and K-nearest neighbors. We trained and tested our datasets using four different scenarios: 60:40, 70:30, 80:20, and 90:10. Our experimental results demonstrate the superiority of the RFO-CNN model across all scenarios, outperforming the benchmark BERT model and other machine learning models in terms of accuracy and F1-score.