Dissemination Of News Research Articles

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.

Tantangan Etika dalam Jurnalisme Digital: Studi Kasus Peran Media Sosial dalam Penyebaran Berita

This research aims to describe the ethical challenges of digital journalism, regarding the role of social media in news dissemination. This research is a qualitative descriptive study. The research results show that the ethical challenges of digital journalism can be seen from the development of media for reporting, starting from print media, mass media, and the current era of digital media where everyone can easily access information (reporting) from various social media today. The ethics of digital journalism can be seen from the non objective reporting that online journalism conveys news instantly and superficially. In fact, this challenge is the news that is conveyed now, it is difficult to know the facts of the news that is uploaded on various social media. One of them is fake news (hoax) which occurs due to a lack of reporting accuracy and journalists' instantaneousness in conveying news/information. The issue of accuracy is now a dilemma for online media which is pursuing speed in publishing information in real time, demanding it be fast but at the same time demanding it be accurate. These many demands often give rise to new problems. Not a few online media take shortcuts, namely by prioritizing speed over accuracy. The practice of self-plagiarism is widespread by repeating sentences in previous reports to recycle them as if they were new news. In certain cases, real-time online media began to emerge which reported quickly on an event, even though he and his team never went to the field to collect the news. Then create news titles or information that are clickbait (excessive titles or titles that sometimes do not match the existing news). These are the challenges faced by journalists in the advancement of technology and social media, sometimes resulting in news producers or readers consuming practical news that lacks accuracy.

Navigating the Intersection of Post-Truth and Sustainability in Journalism: Challenges Facing Sustainable Journalism in Northern Cyprus Media

This study explores the challenges facing sustainable journalism practices in the post-truth era through interviews conducted with journalists in Northern Cyprus. Based on the perspectives of professionals in the field, the aim is to understand current issues related to the feasibility of ethical and sustainable journalism. The study indicates that challenges specific to the media field, compounded by the post-truth effect, relegate sustainable journalism practices to the sidelines. In this context, essential measures for the enduring success of media organizations include strategies like diversifying revenue sources, embracing digital subscription models, investigating alternative business models, enforcing transparent editorial policies, and actively involving readers. The study also reveals the effects of financial difficulties such as low salaries, limited budgets, and financial constraints on the professional practices of journalists through interviews. Furthermore, through interviews, it elucidates how technological changes, the rise of digital media, and the rapid dissemination of news affect journalists’ information control processes and knowledge management strategies. Finally, while contributing to our understanding of how journalists in Northern Cyprus confront challenges in the post-truth era, this study presents the factors that may have a negative impact on sustainable journalism practices through an example and offers insights into common problems that may be encountered in this regard.

Agent-based simulation of fake news dissemination: the role of trust assessment and big five personality traits on news spreading

Agent-based simulation of fake news dissemination: the role of trust assessment and big five personality traits on news spreading

Old age is also a time for change: trends in news intermediary preferences among internet users in Canada and Spain

The social distancing imposed by the Covid-19 pandemic accelerated the digitalisation of societies, which also influenced habits related to the consumption and dissemination of news. In this context, older individuals are often blamed for contributing to disinformation, which is associated with the echo chambers fostered by social media. Mass media, social media and personal communication tools act as mass, social or personal intermediaries when it comes to keeping up to date with the news. This paper analyses the preferred intermediaries of older online adults (aged 60 and over) for following the news and how they change over time. We analysed two waves of an online survey-based longitudinal study conducted in Canada and Spain, before Covid-19 pandemic (2016/17), and during Covid-19 (in 2020). We found that most participants exclusively use mass intermediaries or combine mass with social and personal intermediaries to keep abreast of the news. However, only 28% of respondents inform themselves exclusively through the alleged echo chambers of social and personal intermediaries. Results also show that media ecologies evolve in different directions, and, despite the forced digitalisation driven by the pandemic, digital media usage did not always increase or evolve towards newer technologies. This paper contributes to understanding the diverse intermediaries used by older adults to obtain news and how such media ecologies can contribute to contrasting different sources of information beyond the alleged echo chambers of social media.

Subjective or Objective: A Study of Classifying News Content based on Machine Learning Algorithms

In today's world, self-media as well as streaming media platforms have gradually become the main channels for news dissemination. With the advent of the artificial intelligence era, AI-generated technology will profoundly affect the news industry. Many press releases generated by AI are sufficiently "fake" as well as cascading, so the subjectivity and objectivity of news media are gradually blurred. In order to better standardize and regulate the articles published on self-media platforms, this study is based on 1,000 sports articles and machine learning algorithms to categorize and describe the content of the articles, so as to determine whether the articles are objective or not. At the end of the study, we found that the KNN algorithm performed best in classification.

The Utilization of Twitter in The Anticipation of Covid-19 Hoax News in Yogyakarta City

The contemporary era of openness has exerted a significant influence on social media platforms, particularly Twitter. This is mostly due to the platform's ability to facilitate the expression of opinions and the rapid dissemination of news by individuals. The act of expressing opinions necessitates the inclusion of the need to substantiate the validity of the information being conveyed. Data-driven information can be substantiated and expressed. During the ongoing Covid-19 pandemic, a substantial amount of information pertaining to the coronavirus remains ambiguous or has yet to be definitively established. The prevalence of misinformation is on the rise due to individuals' acceptance and dissemination of false information without conducting thorough research to ascertain its veracity. Consequently, the dissemination of false coronavirus information on social media, particularly Twitter, has occurred.

Reinforced Adaptive Knowledge Learning for Multimodal Fake News Detection

Nowadays, detecting multimodal fake news has emerged as a foremost concern since the widespread dissemination of fake news may incur adverse societal impact. Conventional methods generally focus on capturing the linguistic and visual semantics within the multimodal content, which fall short in effectively distinguishing the heightened level of meticulous fabrications. Recently, external knowledge is introduced to provide valuable background facts as complementary to facilitate news detection. Nevertheless, existing knowledge-enhanced endeavors directly incorporate all knowledge contexts through static entity embeddings, resulting in the potential noisy and content-irrelevant knowledge. Moreover, the integration of knowledge entities makes it intractable to model the sophisticated correlations between multimodal semantics and knowledge entities. In light of these limitations, we propose a novel Adaptive Knowledge-Aware Fake News Detection model, dubbed AKA-Fake. For each news, AKA-Fake learns a compact knowledge subgraph under a reinforcement learning paradigm, which consists of a subset of entities and contextual neighbors in the knowledge graph, restoring the most informative knowledge facts. A novel heterogeneous graph learning module is further proposed to capture the reliable cross-modality correlations via topology refinement and modality-attentive pooling. Our proposal is extensively evaluated over three popular datasets, and experimental results demonstrate the superiority of AKA-Fake.

Evaluating the Impact of Social Media News on the Level of Students’ Fear and Anxiety during the COVID-19 Pandemic

Background: The spread of false and inaccurate news on social networks has increased during the COVID-19 pandemic, leading to fear and panic among users globally and posing a threat to their mental well-being. Aim: This study aims to investigate the impact of social networks on inducing fear and panic among students amidst the COVID-19 outbreak. Methods: This cross-sectional study was conducted at Varastegan Institute for Medical Sciences in December 2020, involving a sample of 261 students selected through the available sampling method. The data collection tool utilized was a questionnaire, subjected to content analysis for validation. The questionnaire's reliability, determined through the test-retest method, yielded a score of 0.825. Results: Survey results revealed that virtual social networks such as Telegram (73.9%) and Instagram (65.9%) were the primary platforms for receiving COVID-19 news during the pandemic. Furthermore, 62% of students reported using these networks to access health and COVID-19-related information. Moreover, 36% of respondents indicated experiencing psychological distress due to the dissemination of COVID-19 news. Conclusion: The spread of false information on social media significantly contributes to heightened anxiety and fear among students, leading to increased psychological distress. In order to mitigate the adverse effects of misinformation on social media, it is crucial to educate students on discerning between accurate and inaccurate news. Empowering students with skills to navigate reliable sources can enhance their knowledge and help them avoid unnecessary psychological distress.

Optimization of news dissemination push mode by intelligent edge computing technology for deep learning

The Internet era is an era of information explosion. By 2022, the global Internet users have reached more than 4 billion, and the social media users have exceeded 3 billion. People face a lot of news content every day, and it is almost impossible to get interesting information by browsing all the news content. Under this background, personalized news recommendation technology has been widely used, but it still needs to be further optimized and improved. In order to better push the news content of interest to different readers, users' satisfaction with major news websites should be further improved. This study proposes a new recommendation algorithm based on deep learning and reinforcement learning. Firstly, the RL algorithm is introduced based on deep learning. Deep learning is excellent in processing large-scale data and complex pattern recognition, but it often faces the challenge of low sample efficiency when it comes to complex decision-making and sequential tasks. While reinforcement learning (RL) emphasizes learning optimization strategies through continuous trial and error through interactive learning with the environment. Compared with deep learning, RL is more suitable for scenes that need long-term decision-making and trial-and-error learning. By feeding back the reward signal of the action, the system can better adapt to the unknown environment and complex tasks, which makes up for the relative shortcomings of deep learning in these aspects. A scenario is applied to an action to solve the sequential decision problem in the news dissemination process. In order to enable the news recommendation system to consider the dynamic changes in users' interest in news content, the Deep Deterministic Policy Gradient algorithm is applied to the news recommendation scenario. Opposing learning complements and combines Deep Q-network with the strategic network. On the basis of fully summarizing and thinking, this paper puts forward the mode of intelligent news dissemination and push. The push process of news communication information based on edge computing technology is proposed. Finally, based on Area Under Curve a Q-Leaning Area Under Curve for RL models is proposed. This indicator can measure the strengths and weaknesses of RL models efficiently and facilitates comparing models and evaluating offline experiments. The results show that the DDPG algorithm improves the click-through rate by 2.586% compared with the conventional recommendation algorithm. It shows that the algorithm designed in this paper has more obvious advantages in accurate recommendation by users. This paper effectively improves the efficiency of news dissemination by optimizing the push mode of intelligent news dissemination. In addition, the paper also deeply studies the innovative application of intelligent edge technology in news communication, which brings new ideas and practices to promote the development of news communication methods. Optimizing the push mode of intelligent news dissemination not only improves the user experience, but also provides strong support for the application of intelligent edge technology in this field, which has important practical application prospects.

Users' unverified information-sharing behavior on social media: The role of reasoned and social reactive pathways

Unverified or false information spread by irresponsible users can amplify the dissemination of fake news or misinformation. This phenomenon may not only undermine the credibility of social media platforms but also pose severe consequences for individuals and society. This study applies and extends the prototypical willingness model with the aim of comprehending the reasons, and decision-making process driving users' unverified information-sharing behavior a reasoned and intended pathway or an impulsive and unconscious one. Data from a sample of 646 users were analyzed using Structural Equation Modeling to assess the determinative effect of both the reasoned pathway (attitude toward unverified information-sharing, subjective norm, and perceived behavioral control) and the social-reaction pathway (prototype favorability and similarity). Findings highlight the substantial role of the social-reaction pathway in forecasting users' unverified information-sharing behavior, with prototype similarity and attitude being the dominant predictors. Meanwhile, components of the reasoned pathway, specifically perceived behavior control, and attitude, also exhibited significant contributions toward predicting the behavior. In summary, while a deliberate, reasoned process has some influence, the sharing of unverified information on social media by users is primarily an intuitive, spontaneous response to specific online circumstances. This study therefore offers valuable insights that could aid relevant stakeholders in effectively regulating the spread of misinformation. Against this backdrop, highlighting potential risks associated with sharing unverified information and the negative portrayal of users propagating misinformation may contribute to the development of a more critical perspective toward online information sharing by users themselves.

How does social media knowledge help in combating fake news? Testing a structural equation model

To counter the spread of fake news among social media users in Nigeria, this study modelled the moderating function of social media knowledge. To collect responses from participants throughout Nigeria's geopolitical zones, an online poll was run. The data were analysed using Smart PLS 3.6′s structural equation modelling (SEM). We discovered that among Nigerian social media users, instant news sharing, self-promotion, self-expression, and altruism are factors that contribute to the spread of false news. Particularly, altruism and self-promotion had a stronger impact on the dissemination of fake news. We discovered that among people with greater social media knowledge, the correlations between instant news sharing, self-promotion, self-expression, altruism, and false news sharing are less. This study's conclusion section highlighted how this result adds to both theory and practice.

Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News Recommendation

The emergence of online media has facilitated the dissemination of news, but has also introduced the problem of information overload. To address this issue, providing users with accurate and diverse news recommendations has become increasingly important. News possesses rich and heterogeneous content, and the factors that attract users to news reading are varied. Consequently, accurate news recommendation requires modeling of both the heterogeneous content of news and the heterogeneous user-news relationships. Furthermore, users’ news consumption is highly dynamic, which is reflected in the differences in topic concentration among different users and in the real-time changes in user interests. To this end, we propose a Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News Recommendation (DivHGNN). DivHGNN first represents the heterogeneous content of news and the heterogeneous user-news relationships as an attributed heterogeneous graph. Then, through a heterogeneous node content adapter, it models the heterogeneous node attributes into aligned and fused node representations. With the proposed attributed heterogeneous graph neural network, DivHGNN integrates the heterogeneous relationships to enhance node representation for accurate news recommendations. We also discuss relation pruning, model deployment, and cold-start issues to further improve model efficiency. In terms of diversity, DivHGNN simultaneously models the variance of nodes through variational representation learning for providing personalized diversity. Additionally, a time-continuous exponentially decaying distribution cache is proposed to model the temporal dynamics of user real-time interests for providing adaptive diversity. Extensive experiments on real-world news datasets demonstrate the effectiveness of the proposed method.

가짜뉴스의 생산과 전파, 그리고 규제

Fake news reveals the most stimulating problem of the times. Academic research is expanding as controversy increases, but it is difficult to obtain a comprehensive understanding of fake news unless a large amount of research results are examined one by one due to the complex intertwining of various sub-topics. Therefore, this paper aims to gain a macroscopic understanding of the production, dissemination, and regulation of fake news through a single research result. First, the current major discussions and issues on the concept and scope of fake news were investigated, and the causes and propagation processes of fake news were reviewed to confirm how changes in the media environment and human psychology affect the spread of fake news. It also investigated the controversial discussion on the regulation of fake news and suggested that the regulation should be minimized in terms of freedom of expression and that it is desirable to be implemented in an alternative way, such as technical regulation. It is hoped that this article can contribute to streamlining the theoretical review of the research field, especially the convergence academic field, which finds new ways to effectively manage and regulate fake news by providing a macroscopic understanding of fake news through the above discussion.

Identification and analysis of real and fake news by XGBoost algorithm of machine learning

As the internet develops rapidly, fake news has become increasingly easy to propagate. Numerous academics acknowledge the perilous nature of this phenomenon, particularly in the context of the contemporary post-truth era, highlighting its substantial risk to the public. Hence, the detection and halting of fake news dissemination are absolutely vital. This study utilizes machine learnings eXtreme Gradient Boosting (XGBoost) algorithm to construct a model that can differentiate between genuine and fake news. The model is compared with others utilizing different algorithms and is ultimately selected. The study successfully constructs a model with an accuracy rate of approximately 95% in identifying real and fake news. This model provides the public with a convenient way to differentiate between real and fake news and gradually diminishes the threat of fake news. Additionally, this projects implications extend beyond merely identifying real and fake news. The model can be further developed to detect fake information, providing greater societal benefits.

The use of health education as a tool to combat the spread of fake news about vaccination

Vaccines can be understood as technological advances that aim to induce an immunological response through exposure to certain infectious agents, or specific particles thereof. The immunological response obtained creates memory and is capable of reducing the risk of developing diseases, as well as reducing mortality and complications in the event of an eventual infection. The general objective of this study aims to highlight how nurses' actions in the context of health education can help combat fake news related to vaccination. Fake news gained strength with the advent of social networks, and the spread of fake news increasingly threatens several achievements already achieved, including in the area of health. In this sense, there is an intimate relationship between the dissemination of fake news, the anti- vaccine movement and vaccine hesitancy. This is an investigation carried out through a review of scientific literature, which by definition has a qualitative- descriptive character. For its preparation, the findings of 21 works related to the proposed theme were selected, analyzed and summarized. The time frame covered the period from 2018 to 2023, that is, the last 6 years. Data were obtained through the following databases: Google Scholar, PubMed, VHL/Lilacs and Scopus. It was evident that the growing vaccine hesitancy may be associated with factors such as the population's distrust in relation to scientific information about immunization agents, related to the dissemination of fake news regarding the beneficial and adverse effects of vaccines. Above all, the strength of social networks in disseminating these untruths was demonstrated, and the need for public policy interventions and strategies to combat this misinformation.

A Virtual Battlefield for Embassies: Longitudinal Network Analysis of Competing Mediated Public Diplomacy on Social Media

ABSTRACT Mediated public diplomacy is a critical way of strategic communication for governments to influence global public opinion, especially during international conflicts. Based on the theories of mediatization and digitalization as well as the layered model of communicative space, this study investigates how the United States and Iranian embassies competed on the Chinese social media platform Sina Weibo over 13 months. Following a network ethnography approach, this study conducts longitudinal network analysis and textual analysis. The findings suggest that the macro-layer function (i.e. hashtag) facilitated horizontal dissemination of news about the US-Iran conflict from mainly China’s state-media accounts, increasing public awareness of the issue within a short time. The meso-layer (repost and follower network) and micro-layer (mention and comment) functions helped establish vertical communication facilitated by opinion leaders and crowd-enabled leaders that perpetuated public attention on relevant issues. The two embassies engaged with the Chinese public in different communicative layers. The US embassy interacted with the Chinese publics through reposts and replies, while the Iranian embassy constructed their posts in the form of replies. The Iranian embassy leveraged China’s state media agendas as narrative agency to conduct diplomatic communication by incorporating relevant hashtags in their posts. A layered model of mediated public diplomacy is proposed. The findings deepen our understanding of competing mediated public diplomacy, and contribute to the field of international communication of international conflicts.

The Fourth Estate Unbound: Examining Press Law and Ethics in the Digital Age in Indonesia

In Indonesia, the digital age has transformed the media landscape, ushering in new opportunities and challenges for press freedom, law, and ethics. This paper investigates the evolving role of the press in Indonesia's digital era, with a specific focus on the legal and ethical dimensions. It delves into the implications of online platforms and social media on news dissemination, exploring the regulatory frameworks governing journalism and the ethical dilemmas faced by journalists. Additionally, it examines the impact of digital technology on media ownership, censorship, and the proliferation of misinformation. Through case studies and scholarly research, the paper seeks to provide insights into how Indonesian media can navigate these complexities while upholding press freedom and ethical standards. Key areas of analysis include the historical context of press freedom, integrity in online news reporting, challenges to traditional media outlets, strategies for combating misinformation, and the legal implications of digital journalism. Ultimately, the paper aims to contribute to a deeper understanding of how Indonesia's media can adapt and thrive in the digital age while maintaining their essential role as guardians of democracy and public discourse.

Truths and Tales: Understanding Online Fake News Networks in South Korea

This study investigates the features of fake news networks and how they spread during the 2020 South Korean election. Using actor–network theory (ANT), we assessed the network’s central players and how they are connected. Results reveal the characteristics of the videoclips and channel networks responsible for the propagation of fake news. Analysis of the videoclip network reveals a high number of detected fake news videos and a high density of connections among users. Assessment of news videoclips on both actual and fake news networks reveals that the real news network is more concentrated. However, the scale of the network may play a role in these variations. Statistics for network centralization reveal that users are spread out over the network, pointing to its decentralized character. A closer look at the real and fake news networks inside videos and channels reveals similar trends. We find that the density of the real news videoclip network is higher than that of the fake news network, whereas the fake news channel networks are denser than their real news counterparts, which may indicate greater activity and interconnectedness in their transmission. We also found that fake news videoclips had more likes than real news videoclips, whereas real news videoclips had more dislikes than fake news videoclips. These findings strongly suggest that fake news videoclips are more accepted when people watch them on YouTube. In addition, we used semantic networks and automated content analysis to uncover common language patterns in fake news, which helps us better understand the structure and dynamics of the networks involved in the dissemination of fake news. The findings reported here provide important insights on how fake news spread via social networks during the South Korean election of 2020. The results of this study have important implications for the campaign against fake news and ensuring factual coverage.

Audience engagement in data-driven journalism: Patterns in participatory practices across 34 countries

This study explores what motivates data journalists to engage with audiences and their strategies for incorporating audiences into their work. Building on scholarship on audience engagement and participatory journalism, we investigate how data journalists perceive the role of audience; the stage of the reporting process at which the audience is engaged; and how optimistically or sceptically data journalists view the audience’s capacity to contribute to the data journalism reporting process. Using a news media logics theoretical framework, we find data journalists are primarily motivated by a mixture of professional and audience logics The mixture of these logics aligns with their goals to establish institutional identity and legitimization in society, but increasingly data journalists also emphasise hopes for greater authentic participation from their audiences across the reporting process. Analysis of data gathered from in-depth interviews with data journalists from 34 countries provides a better and broader empirical context for explanation of data journalists’ goals for audience engagement, the tools they use to connect with audiences, and the degree to which those goals are met. Our findings contribute to a clearer explanation of audience engagement motivations and strategies in data journalism and the similarities that emerge across a broad geographic array of data journalism work. With a focus on crowdsourcing, data disclosure, interactivity, and news dissemination as forms of audience engagement, we synthesise a portrait of attitudes about audience engagement from the data journalist’s perspective and highlight global similarities.