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exBAKE: Automatic Fake News Detection Model Based on Bidirectional Encoder Representations from Transformers (BERT)
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.
Journal Article
Facts and opinions
\"In this book, readers will learn the differences between facts and opinions. Carefully-leveled text and vibrant, full-color photographs help readers understand that informed citizenship relies on the critical and responsible usage of media and information. Features reading tips for teachers and parents, table of contents, Take Action! activity, questions to encourage deeper inquiry, glossary, and index\"-- Provided by pblisher.
Book
Trends in combating fake news on social media - a survey
Social media following its introduction has witnessed a lot of scholarly attention in recent years due to its growing popularity. These various social media sites have become the mecca of information because of their less costly and easy accessibility. Although these sites were developed to enhance our lives, they are seen as both angelic and vicious. Growing misinformation and fake content by malicious users have not only plagued our online social media ecosystem into chaos, but it also meted untold suffering to humankind. Recently, social media has witnessed a reverberation amid the proliferation of fake news which has made people reluctant to engage in genuine news sharing for fear that such information is false. Consequently, there is a dire need for these fake content to be detected and removed from social media. This study explores the various methods of combating fake news on social media such as Natural Language Processing, Hybrid model. We surmised that detecting fake news is a challenging and complex issue, however, it remains a workable task. Revelation in this study holds that the application of hybrid-machine learning techniques and the collective effort of humans could stand a higher chance of fighting misinformation on social media.
Journal Article
The relationship of artificial intelligence (AI) with fake news detection (FND): a systematic literature review
Purpose The purpose of this study is to identify the relationship between artificial intelligence (AI) and fake news detection. It also intended to explore the negative effects of fake news on society and to find out trending techniques for fake news detection. Design/methodology/approach “Preferred Reporting Items for the Systematic Review and Meta-Analysis” were applied as a research methodology for conducting the study. Twenty-five peer-reviewed, most relevant core studies were included to carry out a systematic literature review. Findings Findings illustrated that AI has a strong positive relationship with the detection of fake news. The study displayed that fake news caused emotional problems, threats to important institutions of the state and a bad impact on culture. Results of the study also revealed that big data analytics, fact-checking websites, automatic detection tools and digital literacy proved fruitful in identifying fake news. Originality/value The study offers theoretical implications for the researchers to further explore the area of AI in relation to fake news detection. It also provides managerial implications for educationists, IT experts and policymakers. This study is an important benchmark to control the generation and dissemination of fake news on social media platforms.
Journal Article
Fake news : falsehood, fabrication and fantasy in journalism
\"Fake News: Falsehood, fabrication and fantasy in journalism examines the causes and consequences of the 'fake news' phenomenon now sweeping the world's media and political debates. Drawing on three decades of research and writing on journalism and news media, leading scholar Brian McNair engages with the fake news phenomenon in accessible, insightful language designed to bring clarity and context to a complex and fast-moving debate.McNair presents fake news not as a cultural issue in isolation but rather as arising from, and contributing to, significant political and social trends in twenty-first century societies. Chapters identify the factors which have laid the groundwork for fake news' explosive appearance at this moment in our globalised public sphere. These include the rise of relativism and the crisis of objectivity, the role of digital media platforms in the production and consumption of news, and the growing drive to produce online content which attracts users and generates revenue. The book also considers the decline of trust in journalism, and the how the traditional left critique of 'dominant ideology' and 'ruling elites' in media has been appropriated by the alt-right, nationalists and populists all over the world.This book rejects the left-right division in discussion of what is and is not 'fake news'. Rather, it aims to provide students, teachers, journalists and general readers with the tools necessary to navigate the digital journalism landscape in the era of President Donald Trump, and to filter out the 'fact' from the 'fake' in their news. \"--Provided by publisher.
Book
Combating Fake News on Social Media with Source Ratings: The Effects of User and Expert Reputation Ratings
As a remedy against fake news on social media, we examine the effectiveness of three different mechanisms for source ratings that can be applied to articles when they are initially published: expert rating (where expert reviewers fact-check articles, which are aggregated to provide a source rating), user article rating (where users rate articles, which are aggregated to provide a source rating), and user source rating (where users rate the sources themselves). We conducted two experiments and found that source ratings influenced social media users' beliefs in the articles and that the rating mechanisms behind the ratings mattered. Low ratings, which would mark the usual culprits in spreading fake news, had stronger effects than did high ratings. When the ratings were low, users paid more attention to the rating mechanism, and, overall, expert ratings and user article ratings had stronger effects than did user source ratings. We also noticed a second-order effect, where ratings on some sources led users to be more skeptical of sources without ratings, even with instructions to the contrary. A user's belief in an article, in turn, influenced the extent to which users would engage with the article (e.g., read, like, comment and share). Lastly, we found confirmation bias to be prominent; users were more likely to believe - and spread - articles that aligned with their beliefs. Overall, our results show that source rating is a viable measure against fake news and propose how the rating mechanism should be designed.
Journal Article
Fake news : separating truth from fiction
\"While popularized by President Donald Trump, the term \"fake news\" actually originated toward the end of the 19th century, in an era of rampant yellow journalism. Since then, it has come to encompass a broad universe of news stories and marketing strategies ranging from outright lies, propaganda, and conspiracy theories to hoaxes, opinion pieces, and satire--all facilitated and manipulated by social media platforms. This title explores journalistic and fact-checking standards, Constitutional protections, and real-world case studies, helping readers identify the mechanics, perpetrators, motives, and psychology of fake news. A final chapter explores methods for assessing and avoiding the spread of fake news\"-- Provided by publisher.
Book
A comprehensive survey on machine learning approaches for fake news detection
The proliferation of fake news on social media platforms poses significant challenges to society and individuals, leading to negative impacts. As the tactics employed by purveyors of fake news continue to evolve, there is an urgent need for automatic fake news detection (FND) to mitigate its adverse social consequences. Machine learning (ML) and deep learning (DL) techniques have emerged as promising approaches for characterising and identifying fake news content. This paper presents an extensive review of previous studies aiming to understand and combat the dissemination of fake news. The review begins by exploring the definitions of fake news proposed in the literature and delves into related terms and psychological and scientific theories that shed light on why people believe and disseminate fake news. Subsequently, advanced ML and DL techniques for FND are dicussed in detail, focusing on three main feature categories: content-based, context-based, and hybrid-based features. Additionally, the review summarises the characteristics of fake news, commonly used datasets, and the methodologies employed in existing studies. Furthermore, the review identifies the challenges current FND studies encounter and highlights areas that require further investigation in future research. By offering a comprehensive overview of the field, this survey aims to serve as a guide for researchers working on FND, providing valuable insights for developing effective FND mechanisms in the era of technological advancements.
Journal Article