Dissemination Of News Research Articles

Post-truth Era of Political Marketing: Manipulation Techniques and Their Impact on Public Opinion and Electoral Integrity in the USA

The study focused on the phenomenon of political marketing, political communication, and their application in world politics during the post-truth era with an emphasis on the territory of the United States of America. The aim of the study was to identify several aspects of these disciplines during the dynamic technological progress which makes it possible to develop misleading manipulative techniques and spread disinformation. The emerging issues of the current state of the online sphere such as abuse of personal data for one's own political gain, dissemination of false news that radicalizes the public and threats to the security of free and fair elections and a democracy relate to other concerns such as a polarized society and electoral integrity. Politics has always been, and will continue to be, an infinite number of shades of grey. Absolute honesty and elimination of negative advertising or propaganda is neither realistic nor desirable, though we consider it important to strive for more ethical election campaigns. The conclusion of this article can be applied to other studies that will continue the research on the given topic and will respond to the current challenges of democracy.

Analysis of Communication Characteristics and Social Impact of Short Video News

Due to the rapid development of science and technology, new media are more and more prevalent, and the mode of news dissemination has also undergone great changes. As a new type of news and information dissemination carrier, short video has rapidly become an important way for the public to obtain news and information by virtue of the advantages of concise content, rapid dissemination, multiple visual and sound experience, and strong interactivity. This paper analyzes the impact of short video news on society from the communication characteristics of short video news, in order to provide useful reference for the development of short video news.

Boosting Institutional Identity on X Using NLP and Sentiment Analysis: King Faisal University as a Case Study

Universities increasingly leverage social media platforms, especially Twitter, for news dissemination, audience engagement, and feedback collection. King Faisal University (KFU) is dedicated to enhancing its institutional identity (ID), grounded in environmental sustainability and food security, encompassing nine critical areas. This study aims to assess the impact of KFU’s Twitter interactions on public awareness of its institutional identity using systematic analysis and machine learning (ML) methods. The objectives are to: (1) Determine the influence of KFU’s Twitter presence on ID awareness; (2) create a dedicated dataset for real-time public interaction analysis with KFU’s Twitter content; (3) investigate Twitter’s role in promoting KFU’s institutional identity across 9-ID domains and its changing impact over time; (4) utilize k-means clustering and sentiment analysis (TFIDF and Word2vec) to classify data and assess similarities among the identity domains; and (5) apply the categorization method to process and categorize tweets, facilitating the assessment of word meanings and similarities of the 9-ID domains. The study also employs four ML models, including Logistic Regression (LR) and Support Vector Machine (SVM), with the Random Forest (RF) model combined with Word2vec achieving the highest accuracy of 100%. The findings underscore the value of KFU’s Twitter data analysis in deepening the understanding of its ID and guiding the development of effective communication strategies.

Interactive cue matters: The moderation role of situational factors in the effects of user comments on news sharing

Communicators often find it challenging to prioritize the public and manage their comments during risk communication. This study explored the effects of comments as interactivity cues on news diffusion while considering situational factors under the framework of the Situational Theory of Problem Solving in the context of the US-China trade conflict. For this purpose, the researchers conducted an experiment to investigate the effects of interactivity cues and public segmentations on news sharing. The findings suggest that comments elicit different news-sharing behaviors among different segments of the public. The aware public shares more news than the activists/active public who are more likely to share when exposed to news posts with disabled comments than those with enabled comments. The results regarding the different segments of the public suggest the absence of the latent public in hot issues. Furthermore, the results suggest that message attributes have a limited effect on individuals’ information-processing behavior unless considering situational factors. The theoretical contributions and practical implications for communicators are further discussed.

Modeling the random spreading of fake news through a two‐dimensional time‐inhomogeneous birth‐death process

We consider a two‐dimensional time‐inhomogeneous birth‐death process to model the time‐evolution of fake news in a population. The two components of the process represent, respectively: (i) the number of individuals (say spreaders) who know the rumor and intend to spread it and (ii) the number of individuals (say inactives) who have forgotten the rumor previously received. We employ the probability generating function‐based approach to obtain the moments and the covariance of the two‐dimensional process. We also analyze a new adimensional index to study the correlation between the two components. Some special cases are considered in which both the expected numbers of spreaders and inactives are equal to suitable sigmoidal curves, which are often adopted in modeling growth phenomena. Finally, we provide an application based on real data related to the diffusion of fake news, in which the optimal choice of the sigmoidal curve that fit the datasets is based on the minimization of the mean square error and of the relative absolute error.

Research Paper on Exploring the Landscape of Recommendation Systems: A Comparative Analysis of Techniques and Approaches

The field of recommendation systems has witnessed a profound evolution since its inception with Grundy, the first computer-based librarian, in 1979. From its humble beginnings, recommendation systems have become integral to various facets of daily life, particularly in e-commerce, thanks to breakthroughs like Amazon’s Collaborative Filtering in the late 1990s. This led to widespread adoption across diverse sectors, prompting significant research interest and investment, exemplified by Netflix’s renowned recommendation system contest in 2006. Today, recommendation systems employ various techniques such as Hybrid Filtering, Content-Based Filtering, Demographic Filtering, and Collaborative Filtering catering to personalized information needs across industries like entertainment, education, and healthcare. Moreover, emerging types of recommendation systems, including Knowledge-Based, RiskAware, Social-Networking, and Context-Aware, further broaden their applicability, addressing specific user needs and preferences. Leveraging machine learning and AI algorithms on big data, recommendation systems have become a quintessential application of big data analytics, enhancing user experience and engagement in domains like e-learning, tourism, and news dissemination. However, scaling recommendation systems present challenges due to the exponential growth of input data, necessitating strategies like Dimensionality Reduction and cluster-based methods. Integrating multiple recommendation algorithms enhances system complexity, requiring careful consideration of algorithm selection, performance monitoring, and maintenance. Transparency and explanation mechanisms become crucial in complex systems to foster user trust and understanding. Despite challenges, recommendation systems continue to drive innovation, delivering personalized recommendations and enriching user experiences across various domains.

Influences on COVID-19 Vaccine Adherence among Pregnant Women: The Role of Internet Access and Pre-Vaccination Emotions.

The onset of the COVID-19 pandemic brought about global uncertainties and fears, escalating the dissemination of fake news. This study aims to analyze the impact of fake news on COVID-19 vaccine adherence among pregnant women, providing crucial insights for effective communication strategies during the pandemic. A cross-sectional, exploratory study was conducted with 113 pregnant women under care at a Women's Health Reference Center. Data analysis included relative frequency and odds ratio to assess the relationship between sociodemographic and behavioral variables regarding vaccination. In the behavioral context of vaccination, internet access shows a significant association with decision-making, influencing vaccine refusal due to online information. Nuances in the odds ratios results highlight the complexity of vaccine hesitancy, emphasizing the importance of information quality. Pre-vaccination sentiments include stress (87.61%), fear (50.44%), and anxiety (40.7%), indicating the need for sensitive communication strategies. Results revealed that pregnant women with higher education tend to adhere more to vaccination. Exposure to news about vaccine inefficacy had a subtle association with hesitancy, while finding secure sources was negatively associated with hesitancy. The behavioral complexity in the relationship between online information access and vaccination decision underscores the need for effective communication strategies. In the face of this challenging scenario, proactive strategies, such as developing specific campaigns for pregnant women, are essential. These should provide clear information, debunk myths, and address doubts. A user-centered approach, understanding their needs, is crucial. Furthermore, ensuring information quality and promoting secure sources are fundamental measures to strengthen trust in vaccination and enhance long-term public health.

FATHOM OUT THE NUANCES OF SOCIAL MEDIA LITERACY AND ITS PRACTICAL ALLUSIONS TO MISINFORMATION – A REVIEW

Digital media has become an integral part of our daily lives, connecting people from all around the world in a matter of seconds. In recent years, social media has revolutionized the flow of information and helped people to connect, share, and discuss things at their fingertips. However, this rapid exchange of information has also given rise to a concerning phenomenon – the spread of misinformation. "Misinformation" has led to a wide range of negative consequences, such as political polarization, social unrest, and erosion of trust in traditional media. This review explores the importance of social media literacy, its advantages, impacts, and effects, along with allusions to misinformation. The methodology includes a systematic search of literature in the databases from Internationally double-blinded peer-reviewed journals between 2016 and 2022, in English that also includes scientific articles. A total of 64 articles were obtained. A selection process of articles took place, applying certain inclusion and exclusion criteria, resulting in a total of 10 articles being selected. The findings signify that the conception of social media literacy has just started and more research is needed to identify its practical and theoretical implications. This is linked to the content and competencies, critical thinking, content, and context of the information, and the time and literacy level and fear of missing out on the user. Socio-emotional competencies, literacy level, and time spent stand out since social media is a frequent place of interaction between people. Thus, it is crucial to develop social media literacy skills to combat the dissemination of fake news and ensure a more responsible citizen and informed society.

Journalists’ authority and its bounded trade; Twitter, journalists, and boundary work in contemporary France’s music scene

Journalists, it is widely admitted, are engaged in new forms of boundary work on social media platforms, seeking to uphold their influence over news dissemination. This study focuses on music festivals as a case study to examine journalists' endeavors in maintaining their authority on social media. We analyze Twitter coverage of music festivals in France during the summer of 2018, systematically collecting data from 16 festivals of varying sizes and musical genres. Through this analysis, we investigate journalists' engagement and evaluate the trading of authority with other stakeholders through mentioning practices. Our findings challenge the prevailing notion of journalists as primary arbiters of authority on social media platforms. Despite their conspicuous presence during music festivals on Twitter, journalists emerge as relatively passive participants compared to other stakeholders in the music scene. Moreover, their ability to assert or receive authority from the broader public sphere is limited. This study sheds light on the bounded nature of journalists' boundary work on social media platforms, emphasizing the evolving dynamics of authority within digital information ecosystems.

Comparative analysis of translation platforms: Accuracy, fluency, and unambiguity from a technological perspective

This study aims to evaluate and compare the performance of leading translation platforms (Youdao, Google, Baidu, and ChatGPT) for translating news texts. The evaluation focuses on three key dimensions: accuracy, fluency, and unambiguity. Each platform was tested by translating a Chinese news article, and their outputs were analyzed for their handling of specialized terminology, linguistic fluency, and cultural adaptability. The findings reveal significant performance differences, reflecting varying levels of technological maturity, data quality, and cultural adaptability among the platforms. ChatGPT emerged as the top performer due to its high accuracy and fluency, while Youdao, Baidu, and Google exhibited varying strengths and limitations in handling complex news texts. Ultimately, the results highlight the importance of continuous technological innovation and theory-practice integration in improving translation quality, particularly for accurate and effective global news dissemination.

Intervention analysis for fake news diffusion: an evolutionary game theory perspective

Intervention analysis for fake news diffusion: an evolutionary game theory perspective

Snapping the news: Dynamic gatekeeping in a public service media newsroom reaching young people with news on Snapchat

The relationship between news media and social media platforms is complex, involving dependencies and conflicts. Being present on these platforms offers benefits, such as increased visibility and engagement, particularly with younger audiences. However, platforms also function as gatekeepers through guidelines and algorithmic filtering, impacting the spreading of news media content. Snapchat, popular among young audiences, has been adopted by many traditional news outlets as a more playful distribution channel. Limited research exists on how newsrooms navigate Snapchat’s guidelines and the consequences of violating these, including practices like “flagging,” and the interplay between editorial decisions and algorithms. This study relies on an ethnographic fieldwork at the Norwegian Public Service Broadcaster’s (NRK) editorial group “NRK UNG” (NRK Youth) publishing news to young audiences on Snapchat. The study expands on gatekeeping theory, incorporating recent advancements, and illustrates how the journalists navigate Snapchat’s algorithmic gatekeeping. The study thus highlights the interplay between human news judgment and algorithmic news dissemination on Snapchat at various stages of the news production process.

Developing a Stressor Strain Outcome Model That Predicts Fake News Sharing Behaviour on Social Media: The Mediating Role of Social Media Exhaustion

Despite the growing corpus of knowledge on fake news sharing, nothing much is known about the relationship between the dissemination of fake news and social media exhaustion from an empirical standpoint. There is limited knowledge that exists to show if social media exhaustion could contribute to fake news-sharing behaviour. To address the gaps in the literature, this study created and evaluated a stressor strain outcome model that forecasts social media spread of fake news. It looked at the contributing elements of social media exhaustion before connecting it to disseminating fake news online. It also looked at how social media exhaustion functions as a mediator between its antecedents and fake news-sharing activity. Data was obtained from 1650 social media users using an online survey. Smart PLS structural equation modelling was used to examine the data. Our results, which drew on the stressor strain outcome model, demonstrated that, in the context of fake news-sharing behaviour, social media usage intensity is the most important predictor of social media exhaustion. Social media exhaustion was also predicted by information irrelevance, social media overload, the fear of missing out, and information overload. Also, social media tiredness predicts how people would share misleading news. Further research indicates that the link between information relevance, social overload, fear of missing out, information overload, and the dissemination of fake news is fully mediated by social media exhaustion. Conversely, it partially mediated the link between the frequency of using social media and the dissemination of false information. Our research provides recommendations to social media businesses and developers who wish to reduce concerns about social media tiredness and the spread of fake news.

Analysis of the Success of the "Central Kitchen" All-media Platform in the Context of Media Fusion

Media fusion has become an important trend in the contemporary news and communication industry, which benefits from the rapid development of technology and the push for digital transformation. Media fusion has not only changed the way and form of news dissemination, but also profoundly influenced the way and habits in which the public obtains information. Drawing on the context of media fusion, this paper examines the practical conditions of media fusion from the perspectives of positive and negative factors, and integrates the specific concept of a "Central Kitchen" to explore its innovative performance on the all-media platform. Through analysis, it is found that innovative performance encompasses cross-media integration, reengineering of news production processes, data mining, and intelligent processing, as well as socialization and decentralization. This paper also synthesizes the experiences and insights gained from the all-media platform "central kitchen" in today's media integration. As media convergence progresses, traditional audience identities are gradually transformed through interactions between production and consumption, impacting news content production methods. Furthermore, with deepening levels of media integration, there is an increasing demand for higher capabilities among media practitioners, necessitating collaboration across all forms of media to minimize news production costs while enhancing communication effectiveness.

Not Only Social Networks: Channels of Dissemination of Fake News in the Views of the Population

This article presents an analysis of the problem of the spread of fake news in modern society. The dissemination of fictitious, unreliable information harms public trust, leading to uncontrollable consequences. The purpose of this work is to study the channels of media consumption and to identify the most problematic of them in the context of the spread of fake news. It is based on the results of a mass representative sociological survey of the population of the Republic of Mordovia—a typical representative of the Middle Volga region. During the research, it was found that fake news has penetrated quite firmly into the structures of everyday interaction between people: three out of ten respondents encountered fake news at least several times a month, and two out of ten shared it with others. The key channel for their distribution is social networks, internet publications, television, and Telegram channels. At the same time, these are some of the most popular sources of information. The results obtained can be useful for constructing mathematical models of the spread of fake news, developing measures to counter this kind of content, and developing mechanisms for regulating deliberately false and unreliable data.

Platform Dependency in the Big Tech Era: Lessons from Australian Facebook Blackout and Botswana Implications

The surge in social media usage for news consumption has led to a heightened reliance on these platforms for accessing information worldwide. In Botswana, Facebook has become one of the primary channels for news consumption, mirroring trends seen in various nations. However, the 2021 Australian Facebook conflict exposed the risks associated with over-reliance on such platforms for news dissemination. In this paper, we investigate the phenomenon of platform dependency, specifically examining the implications for Botswana, in the wake of the Australian Facebook news blackout. Employing a stratified sampling approach, data was gathered by tracking the volume of news posts shared across 51 selected Facebook pages throughout February 2021. The findings revealed a noteworthy trend, with an average of 5630 news items posted or shared on the platform within four weeks. This marked a growing reliance on Facebook-exclusive pages for news consumption, with minimal engagement observed on alternative websites. We argue that there is a precarious position of pages solely reliant on Facebook for news sharing, especially amidst escalating big data politics and regulatory measures that target Tech Giants. Consequently, these pages face an uncertain future, potentially hampering access to vital news reporting and information-sharing to the adaptive population. Currently, in Botswana, there is a notable dearth of academic exploration into the regulation of Big Tech and its potential repercussions for the nation. Thus, this study serves as a cornerstone in pursuing this significant area of inquiry.

The use of intelligent real-time image acquisition system using data mining technology in news acquisition and editing practice

Aiming to address the timely dissemination of news information, this work explores the clever utilization of data mining (DM) technology and deep learning (DL) algorithms to construct an intelligent real-time news image acquisition system to meet the urgency of news dissemination needs. First, this work introduces an intelligent real-time news image acquisition system and provides a detailed analysis of its principles and advantages. Throughout this process, the crucial role of DM technology in news image classification and automation is emphasized, especially in dealing with rapidly evolving news events. Next, the work establishes an intelligent real-time news image acquisition model based on DL algorithms, which integrates the essence of DM technology. Through this fusion, the research objective is to enhance the performance of the news image acquisition system to achieve higher real-time and accuracy, which is vital for the swift delivery of news information. Finally, this work investigates the application of the intelligent news image acquisition system in network communication to ensure its adaptability to various network communication scenarios while maintaining accuracy and real-time capabilities. The research results demonstrate that the application of DM technology in combination with DL algorithms can effectively meet the practical needs of the news industry, enhancing the automation of news image processing and enabling faster information delivery to the audience. Notably, the AlexNet model employed performs exceptionally well, achieving recognition rates of up to 99.6% after data augmentation or equalization processing, with an accuracy of 90.9% and a high specificity of 93.38%. This indicates outstanding overall classification accuracy and negative class accuracy, even when distinguishing between news and non-news scenarios. These results clearly underscore the connection between DM technology and news acquisition and editing practices, and emphasize its potential to improve the efficiency and accuracy of real-time information dissemination. The research’s contribution and innovation lie in the fusion of DM technology with DL algorithms to build an intelligent real-time news image acquisition system. This fusion enhances the automation and classification performance of news images, thereby improving the real-time and accuracy of news information. Furthermore, the work strongly emphasizes improving the real-time and accuracy of the news image acquisition system to ensure the swift delivery of information, which is of utmost importance in rapidly changing news events.

Correlation between belief in science and belief in pseudoscience in high school students

Dissemination of news through social media is one of the biggest causes of increasing student’s trust in pseudoscience. The purpose of this correlational study was to analyze the relationship between belief in science and pseudoscience beliefs related to biology in high school students. This research entails a correlational study conducted through a survey methodology. The results of the study show that the ability to believe in science has a significant effect on students' belief in pseudoscience related to biology for high school students. Based on the research, the initial hypothesis of the study was rejected, that there is a positive relationship between belief in science and belief in pseudoscience related to biology, thus indicating that increasing students' confidence in science will also increase students' confidence in pseudoscience related to biology. This is due to the continuous spread of unscientific issues through social media which will affect the increase in trust in pseudoscience. The increase in pseudoscience is due to the lack of student media literacy so that it can affect the way students respond to news that is on social media. The lack of education regarding students' media literacy is a factor in increasing students' confidence in pseudoscience related to biology for high school students.

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.

Human or Not?: An Experiment With Chatbot Manipulations to Test Machine Heuristics and Political Self-Concepts

Chatbots have a growing role to play in political discourse, including in political campaigns, voter mobilization ventures, and dissemination of political news, though chatbots in the political domain are relatively understudied. While testing the machine heuristics and political self-concepts frameworks, we carried out a 2 × 2 experiment where both perceived conversational partner (i.e., bot, human) and topic (i.e., political, casual) were manipulated ( N = 126). During the experiment, participants exchanged chat messages with trained research confederates for 30 min. In support of the machine heuristics and political self-concepts frameworks, participants assigned to human partners reported more positive relationships and higher political interest. Through moderation analysis, liking the partner was found to differ between the perceived partner conditions, with perceived political knowledge varying more in the human conditions. Thus, the experimental findings add nuance to interpersonal (i.e., impression management and social identity theory) and human-computer interaction theories (i.e., machine heuristics and Computers Are Social Actors), and have broader implications for online political interactions and for decisionmakers of online political discourse spaces.