Spread Of News Research Articles

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.

Controlling perpetrators of spreading fake news in the Southern Sumatra regional police

The continuous development of information technology brings significant changes in various fields of community life. Society and the internet have a very complex and significant relationship, especially in terms of using the internet as a means of socialisation and interaction. In the context of social media abuse, people are often trapped in complex issues that lead to hoaxes and hate speech. This study examines policies, control measures, and punishment for hoax spreaders in the South Sumatra Regional Police jurisdiction. This research employs qualitative research using empirical studies and a legal, sociological framework. This research aims to contribute to the corpus of literature and information regarding the perpetrators of spreading false news (hoax) at the South Sumatra Regional Police. The findings of this study reveal that criminal law enforcement against fake news spreaders (hoaxes) in the South Sumatra Regional Police have procedures or efforts to resolve cases of fake news (hoax); the procedure includes receiving reports/complaints, investigations, investigations, sending SPDP (Notice of Commencement of Investigation), sending case files, and transferring suspects and evidence. Law enforcement entities involved in combating fake news are the Police, Prosecutors, and Judges. This research provides the latest data in 2022 regarding cases of spreading fake news (Hoax) in the South Sumatra Regional Police.

Fake News Prediction and Analysis in LIAR Dataset Using Advanced Machine Learning Techniques

Fake news detection and prediction is the crucial research issue in now a day because it is very difficult to know the news authenticity on social media. It has a devastating impact on societies and democratic institutions as online life in these days are one of the principal news hotspots for many individuals around the world because of their minimal effort, simple access, and quickly spread of the unauthorized news. However, measurable ways to deal with battling fake news have been drastically restricted by the absence of named benchmark datasets. Smart machine learning classifiers are used to solve the problem of fake news prediction and classification. The proposed research study works on the LIAR dataset, the open-source available dataset for fake news classification with 12.8K decade-long, hand-labelled short statements in various contexts. The proposed research study has used a novel approach to deal with the fake news prediction accurately and this approach outperforms in this scenario for the same dataset. Naïve Bayes classifier for classification is used to reduce the variance values in the dataset to get rid of the overfitting issue. This classifier shows more improved results than other prior classifiers and the accuracy value was 99%. The proposed research study performed experiments and evaluated through different evaluation measures, the results of accuracy for the Naïve Bayes are best as compared to Random Forest, Decision tree, and Neural Networks are computed for each algorithm. The proposed research study could be applied in real-time applications to deal the fake news prediction in social media and digital media platforms.

Unveiling Implicit Deceptive Patterns in Multi-Modal Fake News via Neuro-Symbolic Reasoning

In the current Internet landscape, the rampant spread of fake news, particularly in the form of multi-modal content, poses a great social threat. While automatic multi-modal fake news detection methods have shown promising results, the lack of explainability remains a significant challenge. Existing approaches provide superficial explainability by displaying learned important components or views from well-trained networks, but they often fail to uncover the implicit deceptive patterns that reveal how fake news is fabricated. To address this limitation, we begin by predefining three typical deceptive patterns, namely image manipulation, cross-modal inconsistency, and image repurposing, which shed light on the mechanisms underlying fake news fabrication. Then, we propose a novel Neuro-Symbolic Latent Model called NSLM, that not only derives accurate judgments on the veracity of news but also uncovers the implicit deceptive patterns as explanations. Specifically, the existence of each deceptive pattern is expressed as a two-valued learnable latent variable, which is acquired through amortized variational inference and weak supervision based on symbolic logic rules. Additionally, we devise pseudo-siamese networks to capture distinct deceptive patterns effectively. Experimental results on two real-world datasets demonstrate that our NSLM achieves the best performance in fake news detection while providing insightful explanations of deceptive patterns.

GAMC: An Unsupervised Method for Fake News Detection Using Graph Autoencoder with Masking

With the rise of social media, the spread of fake news has become a significant concern, potentially misleading public perceptions and impacting social stability. Although deep learning methods like CNNs, RNNs, and Transformer-based models like BERT have enhanced fake news detection. However, they primarily focus on content and do not consider social context during news propagation. Graph-based techniques have incorporated the social context but are limited by the need for large labeled datasets. To address these challenges, this paper introduces GAMC, an unsupervised fake news detection technique using the Graph Autoencoder with Masking and Contrastive learning. By leveraging both the context and content of news propagation as self-supervised signals, our method reduces the dependency on labeled datasets. Specifically, GAMC begins by applying data augmentation to the original news propagation graphs. Subsequently, these augmented graphs are encoded using a graph encoder and subsequently reconstructed via a graph decoder. Finally, a composite loss function that encompasses both reconstruction error and contrastive loss is designed. Firstly, it ensures the model can effectively capture the latent features, based on minimizing the discrepancy between reconstructed and original graph representations. Secondly, it aligns the representations of augmented graphs that originate from the same source. Experiments on the real-world dataset validate the effectiveness of our method.

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.

Uncovering the authorship: Linking media content to social user profiles

The extensive spread of fake news on social networks is carried out by a diverse range of users, encompassing private individuals, newspapers, and organizations. With widely accessible image and video editing tools, malicious users can easily create manipulated media. They can then distribute this content through multiple fake profiles, aiming to maximize its social impact. To tackle this problem effectively, it is crucial to possess the ability to analyze shared media to identify the originators of fake news. To this end, multimedia forensics research has advanced tools that examine traces in media, revealing valuable insights into its origins. While combining these tools has proven to be highly efficient in creating profiles of image and video creators, it is important to note that most of these tools are not specifically designed to function effectively in the complex environment of content exchange on social networks. In this paper, we introduce the problem of establishing associations between images and their source profiles as a means to tackle the spread of disinformation on social platforms. To this end, we assembled SocialNews, an extensive image dataset comprising more than 12,000 images sourced from 21 user profiles across Facebook, Instagram, and Twitter, and we propose three increasingly realistic and challenging experimental scenarios. We present two simple yet effective techniques as benchmarks, one based on statistical analysis of Discrete Cosine Transform (DCT) coefficients and one employing a neural network model based on ResNet, and we compare their performance against the state of the art. Experimental results show that the proposed approaches exhibit superior performance in accurately classifying the originating user profiles.