The emergence of various social networks has generated vast volumes of data. Efficient methods for capturing, distinguishing, and filtering real and fake news are becoming increasingly important, ...especially after the outbreak of the COVID-19 pandemic. This study conducts a multiaspect and systematic review of the current state and challenges of graph neural networks (GNNs) for fake news detection systems and outlines a comprehensive approach to implementing fake news detection systems using GNNs. Furthermore, advanced GNN-based techniques for implementing pragmatic fake news detection systems are discussed from multiple perspectives. First, we introduce the background and overview related to fake news, fake news detection, and GNNs. Second, we provide a GNN taxonomy-based fake news detection taxonomy and review and highlight models in categories. Subsequently, we compare critical ideas, advantages, and disadvantages of the methods in categories. Next, we discuss the possible challenges of fake news detection and GNNs. Finally, we present several open issues in this area and discuss potential directions for future research. We believe that this review can be utilized by systems practitioners and newcomers in surmounting current impediments and navigating future situations by deploying a fake news detection system using GNNs.
•All searchable articles of graph neural network (GNN) for fake news detection are reviewed.•A comprehensive survey of fake news and GNN is provided.•Details of GNN models for fake news detection systems are introduced, categorized, and compared.•To the best of our knowledge, this is the most thorough GNN survey for fake news detection.•Current status of GNN in fake news detection was provided, also future opportunities were highlighted.
The book focuses on how different generations perceive fake news, including young and middle-age groups of people, multiple age groups, university students and adults in general, elementary students, ...children, and adolescents. It provides insights into the different methodologies available with which to research fake news from a generational perspective.
News currently spreads rapidly through the internet. Because fake news stories are designed to attract readers, they tend to spread faster. For most readers, detecting fake news can be challenging ...and such readers usually end up believing that the fake news story is fact. Because fake news can be socially problematic, a model that automatically detects such fake news is required. In this paper, we focus on data-driven automatic fake news detection methods. We first apply the Bidirectional Encoder Representations from Transformers model (BERT) model to detect fake news by analyzing the relationship between the headline and the body text of news. To further improve performance, additional news data are gathered and used to pre-train this model. We determine that the deep-contextualizing nature of BERT is best suited for this task and improves the 0.14 F-score over older state-of-the-art models.
Sustainable education and social networks are two important concepts that are closely related. The combination of sustainable education and social networks can be powerful in promoting positive ...change and creating a more sustainable future. Here are some ways in which these two concepts can be integrated:1. Online learning.2. Awareness raising.3. Collaboration.4. Engagement.5. Sharing of best practices.In conclusion, the integration of sustainable education and social networks has the potential to promote positive change and create a more sustainable future by making education more accessible, engaging, and impactful.
This book explores the challenges that disinformation, fake news, and post-truth politics pose to democracy from a multidisciplinary perspective. The authors analyse and interpret how the use of ...technology and social media as well as the emergence of new political narratives has been progressively changing the information landscape, undermining some of the pillars of democracy.The volume sheds light on some topical questions connected to fake news, thereby contributing to a fuller understanding of its impact on democracy. In the Introduction, the editors offer some orientating definitions of post-truth politics, building a theoretical framework where various different aspects of fake news can be understood. The book is then divided into three parts: Part I helps to contextualise the phenomena investigated, offering definitions and discussing key concepts as well as aspects linked to the manipulation of information systems, especially considering its reverberation on democracy. Part II considers the phenomena of disinformation, fake news, and post-truth politics in the context of Russia, which emerges as a laboratory where the phases of creation and diffusion of fake news can be broken down and analysed; consequently, Part II also reflects on the ways to counteract disinformation and fake news. Part III moves from case studies in Western and Central Europe to reflect on the methodological difficulty of investigating disinformation, as well as tackling the very delicate question of detection, combat, and prevention of fake news.This book will be of great interest to students and scholars of political science, law, political philosophy, journalism, media studies, and computer science, since it provides a multidisciplinary approach to the analysis of post-truth politics.
Over the recent years, the growth of online social media has greatly facilitated the way people communicate with each other. Users of online social media share information, connect with other people ...and stay informed about trending events. However, much recent information appearing on social media is dubious and, in some cases, intended to mislead. Such content is often called fake news. Large amounts of online fake news has the potential to cause serious problems in society. Many point to the 2016 U.S. presidential election campaign as having been influenced by fake news. Subsequent to this election, the term has entered the mainstream vernacular. Moreover it has drawn the attention of industry and academia, seeking to understand its origins, distribution and effects.
Of critical interest is the ability to detect when online content is untrue and intended to mislead. This is technically challenging for several reasons. Using social media tools, content is easily generated and quickly spread, leading to a large volume of content to analyse. Online information is very diverse, covering a large number of subjects, which contributes complexity to this task. The truth and intent of any statement often cannot be assessed by computers alone, so efforts must depend on collaboration between humans and technology. For instance, some content that is deemed by experts of being false and intended to mislead are available. While these sources are in limited supply, they can form a basis for such a shared effort.
In this survey, we present a comprehensive overview of the finding to date relating to fake news. We characterize the negative impact of online fake news, and the state-of-the-art in detection methods. Many of these rely on identifying features of the users, content, and context that indicate misinformation. We also study existing datasets that have been used for classifying fake news. Finally, we propose promising research directions for online fake news analysis.
Fake news on social media has become a serious problem, and social media platforms have started to actively implement various interventions to mitigate its impact. This paper focuses on the ...effectiveness of two platform interventions, namely a content-level intervention (i.e., a fake news flag that applies to a single post) and an account-level intervention (i.e., a forwarding restriction policy that applies to the entire account). Collecting data from China's largest social media platform, we study the impact of a fake news flag on three fake news dissemination patterns using a propensity score matching method with a difference-in-differences approach. We find that implementing a policy of using fake news flag influences the dissemination of fake news in a more centralized manner via direct forwards and in a less dispersed manner via indirect forwards, and that fake news posts are forwarded more often by influential users. In addition, compared with truthful news, fake news is disseminated in a less centralized and more dispersed manner and survives for a shorter period after a forwarding restriction policy is implemented. This study provides causal empirical evidence of the effect of a fake news flag on fake news dissemination. We also expand the literature on platform interventions to combat fake news by investigating a less studied account-level intervention. We discuss the practical implications of our results for social media platform owners and policymakers.
In recent years, with the fast development of the internet and online platforms such as social media feeds, news blogs, and online newspapers, deceptive reports have been universally spread online. ...This manipulated news is a matter of concern due to its potential role in shaping public opinion. Therefore, the fast spread of fake news creates an urgent need for automatic systems to detect deceitful articles. This motivates many researchers to introduce solutions for the automatic classification of news items. This paper proposed a novel system to detect fake news articles based on content-based features and the WOA-Xgbtree algorithm. The proposed system can be applied in different scenarios to classify news articles. The proposed approach consists of two main stages: first, the useful features are extracted and analyzed, and then an Extreme Gradient Boosting Tree (xgbTree) algorithm optimized by the Whale Optimization Algorithm (WOA) to classify news articles using extracted features. In our experiments, we considered the bases of the investigation on classification accuracy and the F1-measure. Then, we compared the optimized model with several benchmark classification algorithms based on a dataset that compiled over 40,000 various news articles recently. The results indicate that the proposed approach achieved good classification accuracy and F1 measure rate and successfully classified over 91 percent of articles.
•Social media, fake news, and COVID-19.•Misinformation on social media has fuelled panic regarding the COVID-19.•Altruism is the strongest predictor of fake news sharing on COVID-19.•Socialization, ...information seeking and pass time predict fake news sharing.•Entertainment is not associated with sharing fake news on COVID-19.
Fake news dissemination on COVID-19 has increased in recent months, and the factors that lead to the sharing of this misinformation is less well studied. Therefore, this paper describes the result of a Nigerian sample (n = 385) regarding the proliferation of fake news on COVID-19. The fake news phenomenon was studied using the Uses and Gratification framework, which was extended by an “altruism” motivation. The data were analysed with Partial Least Squares (PLS) to determine the effects of six variables on the outcome of fake news sharing. Our results showed that altruism was the most significant factor that predicted fake news sharing of COVID-19. We also found that social media users’ motivations for information sharing, socialisation, information seeking and pass time predicted the sharing of false information about COVID-19. In contrast, no significant association was found for entertainment motivation. We concluded with some theoretical and practical implications.
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