News Topics Research Articles

Study of the Framework and Attributes of Ethical Standards in Journalism

It has always been difficult to create fair evaluation criteria for journalists, especially when it comes to judging morality and ethical conduct in news reporting. These guidelines function as yardsticks for gauging the ethics of news topics and the objectivity of journalistic techniques. These criteria are becoming more and more difficult as journalism changes, especially in the digital age. Fostering ethical journalism requires an understanding of its categorization and internal structure. This analysis employs a conceptual framework to explore the evaluation standards of news ethics. The method involves: Literature Review: Examining existing academic literature, ethical codes, and industry guidelines to outline the prevailing evaluation standards in journalism. Thematic Analysis: Identifying and categorizing the components of evaluation standards into normative and quality standards, assessing their relevance to different aspects of journalistic practice. Case Studies: Analyzing specific instances where journalistic ethics were put to the test, allowing for a practical understanding of how these standards function in real-world scenarios. The primary objective of this study is to clarify the internal structure of journalistic ethics evaluation standards and highlight the distinctions between normative and quality standards. By doing so, the analysis aims to provide insights into how these standards can be effectively applied to evaluate news behaviors and moral character in journalism. What is deemed ethical in one context may not hold the same weight in another. This relativity underscores the importance of understanding the specific circumstances surrounding journalistic practices. Historical Context: The evolution of journalistic ethics is tied to historical events and societal changes. Standards that once sufficed may need reevaluation to address contemporary challenges, particularly with the rise of digital media and misinformation. Complex Internal Structure: The internal structure of evaluation standards can be multi-layered, with different levels of criteria applied depending on the context and specific ethical dilemmas faced by journalists. This complexity requires continuous dialogue and adjustment as new ethical challenges arise. Practical Implications: Journalists and media organizations must engage with these standards actively, incorporating them into training and policy development. By fostering an understanding of both normative and quality standards, the industry can promote a more ethical and accountable journalism practice.

Online Writeup Categorization using a Machine Learning Strategy

A variety of online sources produces a huge amount of daily news; thus, it is important to categorize the news items to make the information accessible to consumers easily and quickly scraping is used to gather existing news items from news websites and then categorize them automatically using a variety of classification algorithms. As a result, news categorization is a technique for discovering untracked news themes and providing specific recommendations based on the user's historical interests. The BBC News dataset, which includes articles from five different categories including Business, Entertainment, Politics, Sport, and Technology, is used in this task to discuss various steps in news classification and implement a few algorithmic approaches such as Naive Bayes, Binary Classifier, SVM, Perceptron, and SGD. In the study, results from several classification algorithms are examined, and their accuracy is measured.

The Making of Good News: Discursive Construction of Good News Through News Values

ABSTRACT Good news aims to redress the balance from negative bias and contributes to a more comprehensive form of journalism. This article explores the largely ignored discursive construction of good news through news values. It first investigates the features of good news stories as against top news stories in the mainstream media, and then identifies differences in the discursive practices by the mainstream media and the good news outlets in covering the same events. The findings show that in the mainstream media, top news stories tend to highlight Eliteness and Negativity, whereas good news stories give emphasis to Personalization and Positivity. When covering the same events, reports by the good news outlets show an overemphasis on Positivity, and those by the mainstream media demonstrate a more balanced distribution of the major news values. It is proposed that good news can serve as a valuable complement to top news in the mainstream media. Reports in the mainstream media tend to be more comprehensive and informative, and the relatively shorter reports in the good news outlets pay more attention to the positive aspects of the event that are effective in uplifting the readers’ mood.

"News has various shades": Quantifying and Analyzing Media Bias at Aspect-level Granularity

This research presents a novel approach to dissecting media narratives, with a specific focus on aspect-level granularity and variance in addressing multifaceted news topics. Unlike previous studies, which often concentrate solely on ideological or political biases, our methodology delves deeper, exploring how diverse media outlets navigate the complexities of various news aspects. Through a detailed case study on Indian government policies, we uncover distinctive biases and variations in reporting between Indian and international media. Crucially, our methodology leverages Natural Language Inference (NLI) to identify news aspects and ascertain aspect-level sentiment from news text, enabling scalable and precise quantification of bias across diverse media narratives. Our findings illuminate the multifaceted layers of media coverage, revealing nuanced stances on different aspects of the same topic and the dynamic nature of biases over time. Importantly, our comprehensive framework quantifies media bias through data-driven quantitative evaluation, capturing both selection bias based on news aspect coverage and statement bias based on sentiment polarity. It is relevant to note that in our context, bias is not inherently favourable or unfavourable but rather serves as a quantitative metric to measure divergence in news presentation from the overall average. Thus, our contributions not only enhance understanding of media bias but also introduce a methodology distinguished for its comprehensive and scalable approach to analyzing media narratives, with substantial implications for future research and discussions in this domain.

A Study on Internet News for Patient Safety Campaigns: Focusing on Text Network Analysis and Topic Modeling.

Background/Objectives: This study aimed to identify the main issues related to public patient safety campaigns reflected in Korean online news. This study utilized a text-mining method to identify keywords and topics related to patient safety campaigns. Methods: The data collection period was from 1 January 2022 to 31 December 2023, and 4110 news articles were extracted. Through data preprocessing, 2661 duplicated news and 1213 unrelated news were removed, and 236 news were selected. Using the NetMiner program, keyword co-occurrence frequency calculation, keyword centrality analysis, and topic modeling analysis were performed. Results: The results showed that the most frequently mentioned keywords with high degree centrality, betweenness centrality, and closeness centrality in online news were "hospital", "medical", "medicine", "project", and "treatment". The topics of online news related to the patient safety campaign were "patient-centered care for medical safety", "health promotion projects at a regional institution", "hand hygiene education to prevent infection", "healthcare quality improvement through the Mint Festival", and "safe use of medicines". Conclusions: This study analyzed patient safety campaign news topics using text network analysis and topic modeling. It was confirmed that patient safety campaigns are essential for fostering a patient safety culture, improving medical quality, and encouraging patient participation in hospitals. Therefore, to build a safe medical environment, it is necessary to establish an effective patient safety campaign for not only medical staff providing medical care, but also patients and their caregivers, and for this, cooperation and participation from various professional occupations are necessary.

Forecasting Inflation Using Economic Narratives

We use economic narratives to forecast inflation with a large news corpus and machine learning algorithms. The economic narratives from the full text content of over 880,000 Wall Street Journal articles are decomposed into multiple time series representing interpretable news topics, which are then used to predict inflation. The results indicate that narrative-based forecasts are more accurate than the benchmarks, especially during recession periods. Narrative-based forecasts perform better in long-run forecasting and provide incremental predictive information even after controlling macroeconomic big data. In particular, information about inflation expectations and prices of specific goods embedded in narratives contributes to their predictive power. Overall, we provide a novel representation of economic narratives and document the important role of economic narratives in inflation forecasting.

Attention Spillovers from News to Ads: Evidence from an Eye-Tracking Experiment

The authors investigate the impact of online news content on the effectiveness of display advertising. In a randomized online experiment, participants read news articles randomly paired with brand advertisements. Leveraging nonintrusive eye-tracking technology, the authors measure individual attention to both articles and ads. The authors then measure ad recall, and participants choose between cash and brand-specific vouchers. Heightened attention to articles results in “spillover” attention to ads on the same page, which, in turn, increases both brand recall and purchase probability. The authors also consider the effect of news content type, differentiating between “hard” and “soft” news. They find that advertising next to hard news is at least as effective as advertising next to soft news. This provides evidence against the blunt implementation of “block lists” for sensitive news topics by advertisers. The authors discuss the implications of attention spillovers for firms contemplating investments in engaging news content within the digital advertising landscape.

Voice-Assisted News App using Natural Language Processing

In recent times, the advancements in Artificial Intelligence (AI) and natural language processing (NLP) have enabled the development of novel voice-assisted apps. This study introduces a cutting-edge Voice-Assisted News App that utilizes the capabilities of Alan M, a prominent AI platform that revolutionizes the way people access news. This research endeavor attempts to utilize Alan Al's capabilities to offer a personalized news experience and enhance clarity through voice interactions. Users of the Voice-Assisted News software can effortlessly request and select their preferred news topics, and the computer will generate the most relevant news updates. Through integration with diverse news sources and the application of sophisticated algorithms, the program generates an individualized news stream for every user, ensuring they receive the latest and most relevant information. Users can efficiently access story audio descriptions. The app's speech interface allows users to navigate across different news categories. The NLP algorithms developed by Alan AI offer precise understanding of user demands by ensuring smooth interaction and an authentic conversational experience

The 1975 Icelandic ‘Women’s day off’ in Nordic print media

ABSTRACT In October 1975, the Icelandic women’s movement organized a ‘Women’s Day Off’ (WDO), a one-day strike designed to reveal the societal importance of women’s work. In this article, we explore media coverage of the WDO in the Nordic countries. Through an analytical lens that focuses on media framing and journalistic practices, we analyse differences in the coverage’s scope and content. We also contextualize the coverage against the background of sociopolitical factors that may have affected the cultural filtering of the news, as well as journalistic practices and resources in each country. In doing so, we demonstrate that the coverage relied on each newsroom’s estimation of the event’s newsworthiness for local readers. That news value was influenced by the country in question’s cultural proximity to Iceland, the state of local feminist organizing and public discussions regarding gender equality, and other news topics in circulation at the time. Our analysis is based on a reading of media texts related to the WDO that we gathered using digital interfaces of the national libraries of Norway, Denmark, Sweden and Finland, complemented by a manual search in cases where such digitization was lacking.

Communication Model in Managing Geothermal Power Plant Conflicts on Serang Regency, Indonesia

Aims: The construction of Geothermal Power Plants in several regions in Indonesia is currently always characterized by conflict and opposition from local communities, including in Serang Regency. One of the main issues is the company’s inappropriate communication strategy, resulting in a lack of community acceptance and leading to conflicts. The research focuses on the different stages of conflict, behavior and communication of conflicting parties within managing conflict strategies. Researchers utilize Galtung's conflict model and the convergence-divergence communication theory. Methodology: The research method used is qualitative research through the examination of case studies. This was done because the problems in this research remained unclear, holistic, complex, dynamic and full of meaning. Meanwhile, the data collection techniques used in this research were documentation obtained 73 news published by 35 online media covering statistics on yearly citizen actions and newspaper publications, news topics or discussions, and online news coverage on the construction of the Geothermal Power Plant in Serang regency, Indonesia. Also, with direct field observations and in-depth interviews with several informants at the study location. Results: Based on research findings, the Geothermal Power Plant conflict went through 4 stages, including the initial conflict phase; conflict phase; the trust-building phase leading to conflict resolution; and the conflict resolution phase. Meanwhile, based on Galtung's theory, conflict occurs when there is a discrepancy between the socialization provided by the company and the community’s direct experiences, leading to rejection through various actions. The divergent communication approach utilized by the company towards the affected communities resulted in different interpretations, ultimately causing the root of the conflict. Conclusion: The conflict stages in the Geothermal Power Plant development project in Serang regency are divided into several phases that closely related with communication occurred between company, government and the local communities. Lack of communication formed negative opinions about the project developers, leading them to oppose the construction of the Geothermal project. The convergent communication model combined with other communication models such as open, participatory and supportive communication is considered to be highly effective in addressing conflicts.

“China’s Foreign Aid” and “China’s National Image” in the Eyes of Foreign Media: A Corpus-Based Discourse-Historical Analysis

China’s foreign aid implements the concept of a community with a shared future for mankind and promotes the common progress of China and developing countries. News about China’s foreign aid released by foreign media is quite significant for overseas audiences to perceive China’s image. This study adopts Ruth Wodak’s discourse-historical approach and corpus-based method to interpret foreign news reports on China’s foreign aid. The macro-level news themes, meso-level discourse strategies and national images, as well as micro-level discourse features are explained with examples. Research shows that news themes reveal the objects, fields and methods of China’s foreign aid. Foreign media widely adopt discourse strategies of nomination, predication, argumentation, perspectivation, intensification and mitigation to shape China as a friendly international donor. A few characteristics of news discourse indicate that several media misunderstand or smear China’s foreign aid, which has a negative impact on China’s international public opinion environment. Therefore, building an effective external voice platform to convey China’s international responsibilities and contributions is necessary, which helps establish a favorable international public opinion environment for China’s development and call on other countries to make efforts to reduce human poverty.

The impacts of economy policy uncertainty on peer effects of firms R&D investment: Based on LDA machine learning and regression statistical modeling approach.

The Latent Dirichlet Allocation (LDA) model is used to extract the text themes of newspaper news and construct the Chinese Economic Policy Uncertainty (EPU) Index. On this basis, based on the relevant data of Chinese A-share listed companies from 2008 to 2020, this paper empirically analyzes the impact of EPU on peer effects of firms R&D investment, and finds that EPU will aggravate the peer effects of firms R&D investment. Furthermore, the moderating effect of manager's motivation to maintain reputation on the process of EPU influencing the peer effects of firms R&D investment was tested, and the mechanism of EPU influencing the peer effects of firms R&D investment through financial frictions was verified.

The influence of visual and textual frames on people’s perception of migrants

This study investigated how the combination of textual and visual frames influences the attribution of responsibility for arrest and deportation of undocumented migrants and empathy. A 3 visual frame: (no photo, stereotypical photo, counter-stereotypical photo) × 2 textual frame: (episodic, thematic) between-subjects online experiment revealed that including a counter-stereotypical photograph in both episodic and thematic news stories had a positive effect on empathy for undocumented migrants but did not affect responsibility for arrest and deportation. Adding a stereotypical photograph to an episodic news story significantly reduced societal responsibility for arrest and deportation and decreased the level of empathy.

News Production Management of Liputan6.com as A Top News Portal

News Production Management of Liputan6.com as A Top News Portal

DO LOCAL NEWSPAPERS REFLECT THE LOCALS? A COMPARATIVE STUDY ON LOCAL AND NATIONAL NEWS COVERAGE IN KHULNA

Local newspapers reflect the voices of the periphery as they have direct and close connections with the local community. This study aims to examine the coverage policy of local newspapers in Khulna, with a particular focus on a comparative analysis of local and national news. This study followed a sequential research design, employing content analysis and in-depth interview methods. The content analysis method was used to assess the several aspects of news coverage and six in-depth interviews were conducted to explore the explanation of content analysis. A total of 2,278 news articles from three local newspapers were scanned and manually inputted into a coding sheet. These codes were analyzed with Microsoft Excell-16 and news-theme was visualized with NVivo-11. The analysis was divided into three phases (i.e., Comparative coverage, news theme, and geographical proximity). The major findings indicate that the ratio of local and national news coverage demonstrates minimal variance in terms of both frequency and column-inch metrics. It has been revealed, however, that the majority of advertisements (96.02%) come from local sources but coverage of local news is comparatively lower, accounting for 53.90%. The most covered local news theme is crime (41.77%), while political news gets highest coverage in the national news category which is 29.81%. Additionally, Jessore district, one of the nearest districts of Khulna receives the highest coverage which is 13.74% while Meherpur district, geographically remotest district of Khulna, received the lowest coverage. The majority of local news is capital-centric, with the Dhaka division receiving the maximum coverage at 93.02%. It indicates that local news was not sufficiently prioritized in local newspapers. Even while geographical proximity acted as a significant factor in news coverage, the news is predominantly urban-oriented.

The News Media in Raising Conflicts Tension: A Critical Study Toward News Conflict Between Israel and Palestine

<p class="Default"><em><span lang="TR">This study analyzes the news about conflict between Israel and Palestine in online news media Foxnews.com on May 2021. The characteristics of news from these news media have quite specific characteristics, which is a strong tone, using open visualization, inviting debate, and succeeding in getting thousands of comments from the audience. In this point, even though the news is read by many people, it is also has the potential to exacerbate conflict in the field. This study adopts Teun A. van Dijk’s Critical Discourse Analysis method which develops analysis from the microstructure, superstructure, and macrostructure levels. The micro structure level looks at the local meaning of the news text from the choice of diction, sentence structure, and rhetoric. Then, the super structure level examines the news scheme, consisting of introduction, content and conclusion. Furthermore, macro structural analysis tries to see the global meaning of news texts by looking at news topics and the surrounding social context. The results of the study show that the Israel and Palestine conflict is not only due to the conflict of one of the group carrying out the attack. But at the same time it was also caused by hard conflict news, there was trauma to the victims in the conflict, as well as the global security conditions still not stable until now. </span></em><em><span lang="EN-US"> </span></em></p>

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.

Zooming in on Topics: An Investigation of the Prevalence and Motives for Selective News Avoidance

ABSTRACT Intentional news avoidance is a common behavior that can be influenced by individual, content, and contextual factors. However, prior studies have primarily focused on general news avoidance and neglected selective avoidance of specific topics. Therefore, our study aimed to determine which topics people tend to avoid and the reasons behind this behavior. We conducted an online survey (N = 1071) with open-ended questions to gather information on topic-specific news avoidance and motives. Our systematic quantitative content analysis revealed that the most commonly avoided news topics were related to the pandemic, political issues, and soft news. Participants cited negative emotional reactions and lack of interest as their primary reasons for avoiding news on an individual level, while redundancy and lack of trust were cited as reasons related to content. Logistic regression analyses revealed that specific motives were related to topic-specific avoidance. Issue fatigue was the primary motivator for avoiding news about COVID-19, while anger and lack of trust were the main reasons for avoiding political news. Lack of interest and overload were the motivators for avoiding soft news. Our study highlights the importance of understanding selective news avoidance and provides insights into the reasons why people choose to avoid certain news topics.

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.

Framing News and Framing Migrants: A Qualitative Study

Frames can have an essential role in the (re)production of the ‘shared ideologies’ concerning several social categories, including migrants. The overall aim of this work is to investigate the discursive construction of frames concerning migration, as rhetoric tools (a) connected to editorial orientations; (b) related to both specific events and more general thematic news articles; (c) having socio-cultural and emotional connotations. The qualitative analysis of 235 Italian news articles enabled us to outline different configurations of discursive elements as well as to identify different emotional pathways. Reflections about the critical role of framing in the social knowledge construction are proposed.