Spread Of News Research Articles

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.

Pengaruh Media Sosial Terhadap Partisipasi Politik Masyarakat Kampus

The use of social media has become an integral part of everyday life, including in people's political participation. Campus community political participation is greatly influenced by social media. Social media such as Facebook, Instagram, and WhatsApp have a significant impact on political engagement. People use social media as a political campaign tool, as well as a means of communicating and obtaining political information that is fast, accurate and reliable. This research aims to determine the influence of social media on political participation in campus communities. The method used is descriptive qualitative. The data collection method was carried out by interviewing students. Participants in this research were PPI FUSI UINSU students using structured interviews conducted with 30 PPI FUSI UINSU students. Data is collected and analyzed to identify levels of media use, participation and politics. The research results show that social media is very important for citizen participation, especially the community. The role of social media as a medium for information about politics is quite large. Social media as a communication tool for seeking political information, social media has positive and negative impacts on the political participation of campus communities, especially in increasing political awareness and participation through online information and discussions and the spread of hoax news. With easy access and fast dissemination of information, social media allows students to more easily engage in political discussions, express opinions, and gather support for various issues. Apart from that, social media also helps in mass mobilization to attend demonstrations, elections and other political events. Social media has an important role in increasing the political participation of students in the Fusion Islamic Education Study Program at UIN North Sumatra. As a result, political participation among students has become more active and dynamic, creating a younger generation who is more aware and critical of developing political issues.

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.

Behavioral Patterns of Social Media Users toward Policy: A Scientometric Analysis

This research explores the behavioural patterns of social media users in response to policies, bridging a gap in the literature by providing in-depth insights into the interaction between user behaviour and public policies relating to social media. An important contribution of this research lies in its holistic approach to understanding how social media users respond to various public policies and their impact on their behaviour. This research aims to explore the influence of the interaction between social media and public policy, understanding how user behaviour on these platforms affects responses to government policies. This study uses qualitative research methods with a scientometric approach. The main results underline the crucial role of social media not only as a communication channel but also as a significant influence tool in shaping public opinion and policy. These studies show that social media has a dual potential, which can support professional and community development, as well as influence government policies and actions through social interaction and information dissemination. However, negative impacts are also evident, such as in the spread of fake news that can significantly alter political views. The practical implications of this research emphasise the need for proactive and systematic government strategies in using social media for the dissemination of accurate information and combating fake news, thereby improving the effectiveness of policy communication and responsiveness to public needs.

Factors of Information Literacy Preventing Fake News: A Case Study of Libraries in Developing Countries

Factors of Information Literacy Preventing Fake News: A Case Study of Libraries in Developing Countries

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.

Twitter Visualitation of Healty Catering Food Promotion Strategy

This analysis aims to determine whether there are differences in marketing strategies and which strategy is superior between the two health catering industries. Intermediary of Twitter as a place to retrieve data, where Twitter is a social media with the fastest spread of news through hashtags and trending topics of a phenomenon that is busy being discussed. They use qualitative methods by utilizing Social Network Analysis (SNA) and are assisted by several supporting tools such as Gephi, Wordji, Notepad++, and Dictionaries to make it easier for researchers to analyze the data obtained so that it is concrete and relevant. From the visualization of Twitter user tweet data, the researcher found that Twitter users discussed Yellowfit Kitchen health catering more than Gorygourmet, as evidenced by a large number of data visualizations obtained. Then on average, customers choose to subscribe to health catering Yellowfit Kitchen with the pure aim of dieting. At the same time, healthy catering Gorygourmet customers subscribe to a more nutritious diet to avoid disease.

MERN Implementation in Online Quiz Applications to Recognize and Avoid Social Media Hoaxes

Social media makes it easier for people to get information on the internet by allowing them to do more than just consume it. They can also create, comment on, and share content in different formats. False information that has permeated society can be readily ingested and used as a source of reference. A JavaScript application developed with the MERN stack technology, which combines several technologies—MongoDB, ExpressJS, ReactJS, and NodeJS—all functioning with one programming language. Data from numerous relevant research sources is gathered using qualitative approaches, and the Scrum method is employed to construct the application system. In addition to fostering creativity and innovation, this approach can resolve challenging and adaptive challenges. There is a maximum duration for every Scrum event. Once a sprint has begun, it cannot be stopped or shortened. Another event may be called off if its objectives have been fulfilled. The purpose of this study is to develop a website that offers pertinent tools and data to social media users in an attempt to stop the spread of hoaxes and fake news. The achieved result is that this application provides various quizzes designed to test users' knowledge of hoaxes and online scams.

Benders decomposition algorithms for minimizing the spread of harmful contagions in networks

The COVID-19 pandemic has been a recent example for the spread of a harmful contagion in large populations. Moreover, the spread of harmful contagions is not only restricted to an infectious disease, but is also relevant to computer viruses and malware in computer networks. Furthermore, the spread of fake news and propaganda in online social networks is also of major concern. In this study, we introduce the measure-based spread minimization problem (MBSMP), which can help policy makers in minimizing the spread of harmful contagions in large networks. We develop exact solution methods based on branch-and-Benders-cut algorithms that make use of the application of Benders decomposition method to two different mixed-integer programming formulations of the MBSMP: an arc-based formulation and a path-based formulation. We show that for both formulations the Benders optimality cuts can be generated using a combinatorial procedure rather than solving the dual subproblems using linear programming. Additional improvements such as using scenario-dependent extended seed sets, initial cuts, and a starting heuristic are also incorporated into our branch-and-Benders-cut algorithms. We investigate the contribution of various components of the solution algorithms to the performance on the basis of computational results obtained on a set of instances derived from existing ones in the literature.

The geography of corporate fake news.

Although a rich academic literature examines the use of fake news by foreign actors for political manipulation, there is limited research on potential foreign intervention in capital markets. To address this gap, we construct a comprehensive database of (negative) fake news regarding U.S. firms by scraping prominent fact-checking sites. We identify the accounts that spread the news on Twitter (now X) and use machine-learning techniques to infer the geographic locations of these fake news spreaders. Our analysis reveals that corporate fake news is more likely than corporate non-fake news to be spread by foreign accounts. At the country level, corporate fake news is more likely to originate from African and Middle Eastern countries and tends to increase during periods of high geopolitical tension. At the firm level, firms operating in uncertain information environments and strategic industries are more likely to be targeted by foreign accounts. Overall, our findings provide initial evidence of foreign-originating misinformation in capital markets and thus have important policy implications.

"Pinocchio had a Nose, You have a Network!": On Characterizing Fake News Spreaders on Arabic Social Media

The detection and analysis of fake news and its origins has become a main task associated with the overall objective of social media regulation in recent years. The majority of work has been dedicated towards detecting misinformation with some focus on analyzing the flow of fake news across social networks. However, there is less attention to the characteristics of social media users who consume this fake news. In this work, we investigate the possibility of predicting users' reactions towards fake news and defining some network characteristics for each users' group. We utilized a set of fact-checking websites in the Arab world that report social media posts spreading fake news and the interactions with them. We defined three sets of users: 1) Spreaders, who spread fake news, 2) Checkers, who constantly share fact-checking threads, and 3) Refuters, who respond to fake-news posts declaring their inaccuracy. We build a classifier that uses users' network graph to predict their reactions with an accuracy exceeding 93%. We applied further analysis for the most significant features of each users group and noticed that spreaders interact with more accounts that use their mother tongue, a considerable number of famous state-sponsored accounts, and accounts that get suspended while checkers and refuters interact with more foreign accounts and news-reporting entities. Central nodes in the networks of spreaders were found to be linked with state-sponsored media, whereas central nodes in the networks of checkers included organizations with a cross-cultural nature.

Analisis Daya Pikir Masyarakat Dalam Pengambilan Keputusan Guna Menangkal Berita Hoax Mengenai Pemilihan Umum 2024

Indonesia will hold general elections to elect the president in 2024. Hoax news regarding the election cannot be avoided. The spread of hoaxes is increasingly widespread due to laws that are not yet effective, the use of online media that is not balanced by the abilities of the users, and a lack of public literacy. This research aims to provide solutions in reviewing and developing people’s thinking in overcoming and responding to the challenges of hoax information related to elections and future political issues that are more complex. This research used qualitative methods and data collection techniques through literature studies. This paper uses Herbert Simon’s theory of rational decision making which shows three steps in decision making, namely identifying problems, finding and developing alternative solutions, selecting and choosing each alternative solution. The result of this research is that problem the spread of election hoax news aims to reduce public trust in election management institutions, election participants, and the government. The author provides several solutions that the public can use as a basis for making decisions regarding the circulation of hoax news, namely: (1) self-criticism; (2) increasing digital literacy; (3) involvement of community leaders; and (4) participatory political education. The author chose the second solution, namely increasing digital literacy, as a solution to minimizing the spread of hoaxes. Digital literacy is the basis for other solutions that can help people develop skills for selecting news on social media.

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

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

Graph-Based Interpretability for Fake News Detection through Topic- and Propagation-Aware Visualization

In the context of the increasing spread of misinformation via social network services, in this study, we addressed the critical challenge of detecting and explaining the spread of fake news. Early detection methods focused on content analysis, whereas recent approaches have exploited the distinctive propagation patterns of fake news to analyze network graphs of news sharing. However, these accurate methods lack accountability and provide little insight into the reasoning behind their classifications. We aimed to fill this gap by elucidating the structural differences in the spread of fake and real news, with a focus on opinion consensus within these structures. We present a novel method that improves the interpretability of graph-based propagation detectors by visualizing article topics and propagation structures using BERTopic for topic classification and analyzing the effect of topic agreement on propagation patterns. By applying this method to a real-world dataset and conducting a comprehensive case study, we not only demonstrated the effectiveness of the method in identifying characteristic propagation paths but also propose new metrics for evaluating the interpretability of the detection methods. Our results provide valuable insights into the structural behavior and patterns of news propagation, contributing to the development of more transparent and explainable fake news detection systems.

Supporting best practice in neuroscience nursing

Sam Weaver and Mary E Braine encourage neuroscience nurses to visit the new British Association of Neuroscience Nurses’ (BANN's) website, consider submitting an abstract for the BANN autumn conference this coming October, and spread news of the new DAISY award for extraordinary neuroscience nurses in partnership with BANN.

The Influence of JOMO and Authenticating News Before Sharing on Fake News Sharing Behavior

Easy access to the internet provides many conveniences for its users. Thanks to the convenience provided, information can spread quickly and widely. However, for every advantage there are disadvantages. The easy dissemination of this information can also have negative impacts, one of which is the spread of fake news. Currently, fake news is very easy to find, especially in online media. This research is based on behavioral reasoning theory, which discusses the reasons behind individual behavior and intentions. The purpose of this theory is to find out the factors that influence the spread of fake news. In this research, there are 2 factors studied, JOMO and authenticating news before sharing through perceived believability. By distributing questionnaires to students and analyzed using SmartPLS, it can be concluded that JOMO and authenticating news before sharing have a negative effect on fake news-sharing intentions either through mediation or directly. The results show that individuals will not have the intention to spread fake news even though the person believes that the source of the information they receive is credible. Based on this research, individuals tend to check the veracity of the news they receive before sharing it to avoid fake news-sharing behavior. Apart from that, there are also some individuals who feel happier and more relaxed when they are not connected to information that is being widely reported, this is their reason not to engage in fake news-sharing.

A Survey: Fake News Detection Mobile App Using Machine Learning

Abstract: With the proliferation of digital information, the challenge of identifying and mitigating the spread of fake news has become increasingly crucial. This research paper provides a comprehensive review of existing technologies designed for the detection of fake news, with a particular focus on their adaptation to mobile applications. The study delves into various approaches, including natural language processing, machine learning algorithms, and deep learning models, highlighting their strengths and limitations. Additionally, it explores the integration of user-generated content analysis and social network features in enhancing the accuracy of fake news detection on mobile platforms. By synthesizing insights from recent advancements, this paper aims to inform the development of an effective mobile app for mitigating the impact of fake news in the digital landscape.

Text Mining for News Forecasting on The Turnback Hoax Website

News has been disseminated swiftly via the internet due to the rapid growth of information technology. The rapid spreading of news often confuses because the truth cannot be ascertained. Additionally, online social media is becoming increasingly popular, making it an excellent environment for propagating false information, including misinformation, phony reviews, advertising, rumors, political remarks, innuendo, etc. This study's specific goal is to classify data using a data mining approach model called text mining so that a system can automatically do the classification. As a result, the study will produce a dataset, which can then be used to create an application using data mining's ability to predict breaking news. An application was produced by employing data mining to forecast recent news. This study was able to classify data using a naive Bayes data mining approach model so that a system can automatically do the classification. The study produced an accuracy of 77% obtained with training data of 82%. From 994 contents, the classification of misleading content reached 33.9%, false content as many as 24.85%, imitation content was 13.48%, fake content reached 11.07%, manipulated content was 9.86%, parody content was 3.22%, satire content was 2.31%, and connection content as many as 1.31%. This study then visualizes the results using bar charts and word clouds. This work also produced datasets with the naïve Bayes method of news data and news that has been valid. Afterward, the dataset will be used in making applications to produce prototypes of computer program applications.

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

Media baru: tren berita provokatif ditinjau dari etika komunikasi

This study aims to analyse and explain the urgency of communication ethics in the new media era (new media). This study uses a descriptive qualitative method with a post -positivist approach, where researchers observe and identify the phenomenon of communication ethics in new media (new media) Instagram on the @lambe_turah account naturally. This research data was obtained from the Instagram account @lambe_turah as a source of primary data, as well as from a literature review as secondary data. This research data was analysed through data reduction process, data presentation, and drawing conclusions. The results of this study indicate, with technological developments that allow the spread of fast and broad news on social media platforms, the need to maintain integrity, honesty, and peace in communication becomes more urgent. Communication ethics plays a major role in guiding media actors to verify information carefully, avoid sensationalism, and reduce the risk of provocation to individuals or groups. In dealing with the complexity of new media, enforcement of integrity, honesty, and peace becomes a crucial foundation in ensuring public trust, promoting social harmony, and preventing unwanted conflict escalation. Although the dominance of sensational and provocative news in user interest, ethical principles remain relevant in ensuring that the message distributed is true, useful, and not detrimental. With an emphasis on the principles of communication ethics, we can use new media as a means to strengthen the values of truth, justice, and wisdom in society, as well as facilitate a deeper understanding of various groups.