News Sources Research Articles

News and misinformation consumption: A temporal comparison across European countries.

The Internet and social media have transformed the information landscape, democratizing content access and production. While making information easily accessible, these platforms can also act as channels for spreading misinformation, posing crucial societal challenges. To address this, understanding news consumption patterns and unraveling the complexities of the online information environment are essential. Previous studies highlight polarization and misinformation in online discussions, but many focus on specific topics or contexts, often overlooking comprehensive cross-country and cross-topic analyses. However, the dynamics of debates, misinformation prevalence, and the efficacy of countermeasures are intrinsically tied to socio-cultural contexts. This work aims to bridge this gap by exploring information consumption patterns across four European countries over three years. Analyzing the Twitter activity of news outlets in France, Germany, Italy, and the UK, this study seeks to shed light on how topics of European significance resonate across these nations and the role played by misinformation sources. The results spotlight that while reliable sources predominantly shape the information landscape, unreliable content persists across all countries and topics. Though most users favor trustworthy sources, a small percentage predominantly consumes content from questionable sources, with even fewer maintaining a mixed information diet. The cross-country comparison unravels disparities in audience overlap among news sources, the prevalence of misinformation, and the proportion of users relying on questionable sources. Such distinctions surface not only across countries but also within various topics. These insights underscore the pressing need for tailored studies, crucial in designing targeted and effective countermeasures against misinformation and extreme polarization in the digital space.

Novel Deep Learning Approach for Predicting Stock Market Trends

Stock Market Prediction a Major Achievement in financial markets is precisely forecasting stock market trends, which remains a considerable hurdle. Deep learning, a subset of machine learning, has grown to be a potent tool for untangling complex relationships within data. What distinguishes deep learning is its capacity to handle large amounts of data, unwrinkle difficult characteristics, and adapt to dynamic structures, all while unearthing concealed truths. Deep learning, at its core, is predicated on artificial neural networks that simulate human brain learning and pattern recognition capabilities, making it ideal for analyzing financial data. Through novel profound neural systems, the strategy applies a varied collection of the distributed showcase information, including, but not limited to, authentic stock costs, trading volumes, and calculations extracted from news sources. Keywords— stock advertise patterns, back, profound learning, designs, authentic information, design acknowledgment, sentiments.

Role of Social Media Platforms in the Spread of Fake News in Nyeri County, Kenya

Fake news is widespread in elections, especially during presidential elections. It has the potential to divide public opinion and create a hostile environment in which people feel their voices aren't being heard. The current study aimed to explore the role of social media platforms in the spread of fake news in Nyeri County. The study is anchored in agenda-setting theory. A mixed-methods approach is used. The population comprised adults in Nyeri County, registered journalists, bloggers, and social media experts, as well as politicians in the county. A sample of 195 respondents was calculated using the modified formulas by Fisher. Questionnaires and interviews were used to collect quantitative and qualitative data, respectively. Quantitative analysis consisted of frequencies and percentages using Microsoft Excel. The qualitative data collected was analyzed using content analysis with the help of NVIVO software. The results of quantitative analysis were presented in the form of tables and figures, while those of qualitative data analysis were presented using narration. The study found that social media was the main source of fake news. Facebook and Twitter had the highest prevalence of fake news, with 87% and 72% of the respondents indicating that they witnessed fake news on the apps, respectively. The study therefore concluded that social media was the biggest source of fake news in Nyeri County. The study recommended that social media companies, especially Facebook and Twitter, should take more responsibility by implementing laws to protect against the spread of fake news. This can include flagging fake news and implementing tough penalties for users spreading propaganda on the internet.

Philippine English variation in police blotters

AbstractPhilippine English variation in lexicon is visible in the language of police blotters. In the process of police blotter writing, only the English written document is used as the basis of investigation, as an attachment to court documents and as a news source. To determine the Philippine English variation in the lexicon in the English transcriptions of Cebuano oral complaints, Y. Kachru and Nelson's perspective on creativity and innovation is used to analyse the police blotters. The Philippine English variation that appears in such blotters shows how Philippine English is not a monolithic construct, but is affected by the sociolinguistic realities of the Philippines.

Proposed Marketing Strategy to Increase Sales of Selaras Outdoor

Selaras Outdoor is a family-based restaurant established in Ciamis in 2021. The F&B business sells affordable prices with a variety of Sundanese and western food products. The location of Selaras Outdoor focuses on nature as if enjoying a meal in a small private forest with the sound of nature from a flowing river. Knowing the increasing number of new restaurants/cafes with outdoor themes, Selaras Outdoor faces business problems that occur including sales revenue that continues to fall. The number of visitors who come is decreasing due to the habit of Ciamis and Tasikmalaya residents who will visit when a new restaurant or cafe location opens. Therefore, a strategy is needed to be able to increase revenue which starts from customer satisfaction. The findings from this research will be used to get the best marketing strategy to create a way to increase revenue. Therefore, a research study will be conducted to address the problems experienced by Selaras Outdoor. The business exploration of Selaras Outdoor will be developed based on external (PESTEL, Porter's Five Forces, customers, and competitors) and internal (VRIO, STP, and Service Marketing Mix) analysis. This research uses two types of data, namely primary and secondary data. Primary data will be collected through interviews and observations while secondary data is carried out to strengthen the theoretical basis of each variable studied as well as external and internal analysis in this study. By using triangulation of data collection on what has been obtained from customers who have visited Selaras Outdoor as well as the latest news sources regarding outdoor restaurants, the results show that customer satisfaction has a positive effect on Selaras Outdoor. Based on the external and internal analysis, the researcher conducted a SWOT analysis and identified the root of the problem. From there it developed into several strategies that were combined together to produce two strategies. The researcher recommended one best strategy with weaknesses that can be overcome to overcome the root causes.

Science and Technology Reportage in Selected Nigerian Newspapers: A Study of Punch, The Guardian and VanguardScience and Technology Reportage in Selected Nigerian Newspapers: A Study of <i>Punch, The Guardian and Vanguard</i>

researchers also set out to determine the level of coverage given to science and technology news by the newspapers; to ascertain the story types (genres) used in reporting science and technology by the newspapers; and identify the sources of science and technology news in the select newspapers. This study adopted quantitative content analysis approach, using agenda-setting theory as its theoretical framework. This study found, among others, that the level of coverage of the issue of discourse was low. Again, different story types were used, but the news genre was mostly applied while major sources of science and technology news were private companies and foreign media. In the vein of these findings, the researchers recommended that Nigeria’s newspapers should increase their coverage of science and technology stories. Also, features and opinion articles (by specialists in the field of science and technology) should be used to explain how most science and technology equipment function, and can be utilised.

Analysis of the Present and Future of the Korea Online Platform Regulation Using Latent Dirichlet Allocation

Purpose: This study was conducted to provide a comprehensive understanding of online platform regulation in South Korea. It aims to illuminate key issues related to online platform regulation from 2017 to 2021, exploring the current state and future prospects. The primary goal is to foster social discourse on online platform regulation and to offer valuable insights for policymakers and academics. Design/methodology/approach: The methodology involved collecting articles from a large news database using keywords associated with online platform regulation. This data was analyzed using Latent Dirichlet Allocation (LDA) topic modeling. This technique is instrumental in identifying themes and patterns within large volumes of text data, facilitating the identification of significant themes and trends related to online platform regulation. Findings: The study unearthed a variety of themes. Among the most significant are the impact of the COVID-19 pandemic and its ramifications, political aspects, the roles of major Korean corporations (e.g., Naver and Kakao), the influence of global tech giants, the impact of news and online communities, technology and innovation, and social issues. These themes indicate that online platform regulation in Korea is influenced by a multitude of factors. Research limitations/implications: While this study offers diverse perspectives on online platform regulation within Korea, it has limitations. Reliance on data from a single news source may restrict the study's diversity and depth. Future research should employ a variety of news sources to provide a broader perspective on online platform regulation. Originality/value: This research makes a significant contribution to the academic study and policy formulation in the field by analyzing the key themes and trends related to online platform regulation over time. By exploring the complex aspects of online platform regulation, the study provides new insights for policymakers, researchers, and the general public.

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.

Prime Minister Social Security Schemes - An Investigation into the Well-being of Rural Households

Abstract: Background: In order to establish a universal social security system for Indian citizens, especially the rural, marginalized, and vulnerable groups, the Indian government launched three ambitious social security schemes related to the insurance and pension sectors on May 9, 2015. Objective: This article is an attempt to investigate the effectiveness of these three social security schemes — Atal Pension Yojna, PM Jeevan Jyoti Beema Yojna, and PM Suraksha Beema Yojna, in improving the well-being of the poor households from rural regions and the people from the unorganized section of the country. Methodology: This investigation is entirely based on secondary sources, particularly the research publications on the selected topic and the government documents, reports, policy announcements, and news sources. Findings: Focusing on the identified schemes and their respective benefits, the article critically investigated their achievements along with the various challenges faced in effectively executing these schemes. Conclusion: The researcher highlighted an urgent need for a multi-faceted approach involving policy adjustments, awareness campaigns, and targeted efforts to address the identified challenges in the effective execution of the identified social security schemes.

Unveiling the veil: A critical analysis of news coverage in Sri Lankan print media

Media plays a fundamental role in shaping public opinion and discourse, with print media serving as a primary source of news and information worldwide. This research critically analyzes news coverage within the print media landscape of Sri Lanka, a country marked by diverse socio-political complexities. Through in-depth interviews with journalists, editors, media analysts, and academics, the study examines media bias, agenda setting, framing of conflict and ethnic issues, representation of minorities, and the influence of political affiliations and ownership structures. Thematic analysis reveals pervasive biases, selective reporting, and challenges in equitable representation. The findings underscore the pivotal role of the media in shaping public perception and discourse, while highlighting the imperative of upholding journalistic ethics and professionalism. Addressing these challenges necessitates fostering media pluralism, promoting diversity in ownership, and ensuring adherence to ethical standards. Ultimately, the study contributes to a deeper understanding of media dynamics in complex socio-political contexts and underscores the importance of a free, impartial, and responsible media in fostering informed public discourse and democratic values.

Navigating digital tourism governance: a case study of branding strategies in the Mandalika special economic zones

The purpose of this research is to analyze how digital Tourism on Branding the Mandalika Special Region in West Nusa Tenggara; this investigation uses a descriptive methodology combined with a qualitative approach, as well as previously published literature and online news sources as well as secondary data obtained from related agencies. Most importantly, take advantage of the Nvivo 12 plus program to analyze the data results. The findings of this study indicate that the use of information and communication technology to promote Special Economic Zones through digital tourism, utilizing platforms such as websites, YouTube, Facebook, TikTok, Instagram, and the West Nusa Tenggara Tourism Office website, is a viable strategy for increasing tourism. Indonesian tourism industry. ITDC and National Council of Special Economic Zones websites. Government Regulation Number 52 of 2014 identifies two main focus topics, namely tourism areas and special economic areas, and prioritizes several initiatives. Development management in accordance with statutory regulations. PP Number 40 of 2021 discusses three important things: human resources and infrastructure, two facilities, and SEZ convenience for three commercial organizations. The finding of human resources and infrastructure is considered to be the main driver for branding the Mandalika Special Economic Zone in this policy.

Integrated Financial Analysis and Asset Recommendation

Integrated Financial Analysis and Asset Recommendation

Media and representation of others. Case of Iran in the 2020 Tokyo Olympics

Media and representation of others. Case of Iran in the 2020 Tokyo Olympics

Cable news advertising: Applying formal analysis to uncover current trends in self-promotional marketing

The emergence of 24/7 cable news channels within the US revolutionized news consumption, offering viewers accessibility beyond fixed broadcast schedules. While the phenomenon began with CNN, competitors quickly emerged, targeting different political ideologies and mirroring the heightened polarization that has altered American society. Cable news channels must market themselves effectively to their audiences through distinctive product differentiation strategies. Understanding these approaches provides insights into information dissemination, political communication, and audience engagement dynamics. This paper aims to conduct a formal media analysis of current self-promotional advertisements across the big three cable news networks within the US to derive insights into contemporary differentiation strategies crucial for monitoring the industry’s technological transition. As the rest of the world has begun to wrestle with increased polarization, this analysis will be relevant to anticipating how non-US news sources use ideology and values to position themselves within highly competitive markets.

Automated stance detection in complex topics and small languages: The challenging case of immigration in polarizing news media.

Automated stance detection and related machine learning methods can provide useful insights for media monitoring and academic research. Many of these approaches require annotated training datasets, which limits their applicability for languages where these may not be readily available. This paper explores the applicability of large language models for automated stance detection in a challenging scenario, involving a morphologically complex, lower-resource language, and a socio-culturally complex topic, immigration. If the approach works in this case, it can be expected to perform as well or better in less demanding scenarios. We annotate a large set of pro- and anti-immigration examples to train and compare the performance of multiple language models. We also probe the usability of GPT-3.5 (that powers ChatGPT) as an instructable zero-shot classifier for the same task. The supervised models achieve acceptable performance, but GPT-3.5 yields similar accuracy. As the latter does not require tuning with annotated data, it constitutes a potentially simpler and cheaper alternative for text classification tasks, including in lower-resource languages. We further use the best-performing supervised model to investigate diachronic trends over seven years in two corpora of Estonian mainstream and right-wing populist news sources, demonstrating the applicability of automated stance detection for news analytics and media monitoring settings even in lower-resource scenarios, and discuss correspondences between stance changes and real-world events.

My body, my voice: Analyzing news sources in the Roe v. Wade Reversal

In June 2022, the U.S. Supreme Court dissolved federal protection for a woman’s right to an abortion and gave each individual state control over abortion laws. The ruling caused a media frenzy, with news outlets across the United States and the world reporting on the ground-breaking decision. The present study employs a textual analysis of this news coverage in the New York Times and the Wall Street Journal, paying special attention to the people interviewed as news sources. News sources were placed into categories based on occupation and gender and the frequency by which the sources were cited was recorded. Findings suggest that while many of the news sources were women, male politicians’ voices still dominate the narrative of women’s reproductive rights in the United States. Practical implications for journalism, policy and law are discussed as are pathways for future research.

Political Balance in Fox News

In the era of media prosperity, Fox News, as the most significant political news provider in the United States (According to Pew Research Center’s research, 16 percent of participants consider Fox News their primary source of political news, which is higher than any other media), influences the political orientation of voters to manipulate election results through various means, such as overemphasizing, reporters’ interpreting, and disregarding news, etc. In order to make their reporting seem more objective and reliable, politically biased media may gain credibility for themselves by covering more news with different political perspectives during non-significant times, which is called political balancing. In order to verify whether political balancing exists in the mainstream media, sixty Fox News reports during the period of 2016 election were randomly selected and analyzed. According to the result, Fox News put statistically significant more positive coverage of Democrats after the Republican candidate, Donald Trump, won the 2016 election. The findings of this research are substantial since awareness of the media’s political balancing will benefit voters in making rational choices for consequential elections. Meanwhile, political balancing could be necessary for researchers conducting studies of journalism.

Rumor detection based on Attention Graph Adversarial Dual Contrast Learning.

It is becoming harder to tell rumors from non-rumors as social media becomes a key news source, which invites malicious manipulation that could do harm to the public's health or cause financial loss. When faced with situations when the session structure of comment sections is deliberately disrupted, traditional models do not handle them adequately. In order to do this, we provide a novel rumor detection architecture that combines dual comparison learning, adversarial training, and attention filters. We suggest the attention filter module to achieve the filtering of some dangerous comments as well as the filtering of some useless comments, allowing the nodes to enter the GAT graph neural network with greater structural information. The adversarial training module (ADV) simulates the occurrence of malicious comments through perturbation, giving the comments some defense against malicious comments. It also serves as a hard negative sample to aid double contrast learning (DCL), which aims to learn the differences between various comments, and incorporates the final loss in the form of a loss function to strengthen the model. According to experimental findings, our AGAD (Attention Graph Adversarial Dual Contrast Learning) model outperforms other cutting-edge algorithms on a number of rumor detection tasks. The code is available at https://github.com/icezhangGG/AGAD.git.

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.

ВЛИЯНИЕ ИНФОРМАЦИОННОГО СТРЕССА НА ПСИХИЧЕСКОЕ ЗДОРОВЬЕ НАСЕЛЕНИЯ

The abundance of diverse information accompanied by information noise can be an additional and powerful stressor, especially if the assimilation and processing of this information is carried out in a time-limited environment. Information streams created by gadgets, reflecting the activity of a person in social networks, his communications, his news sources, are not a harmless source of leisure and recreation. They require the inclusion of sensory systems and central nervous system structures, which are very energy-intensive. Manifestations of information fatigue are clinically similar to stress symptoms, and have common neurobiological mechanisms based on adaptation to changes or new challenges. The emergence of new phenomena of interaction with information space and technogenic environment (FOMO) reflects the influence of information transmitted by gadgets on the mental health of the population. Cognitive flexibility, goal-setting, will-based control, priority-setting, and gadget-detox (gadget regulation and informed information consumption) are all necessary skills for successful interaction with and operation of information flows.