Fake News Research Articles

Supersharers of fake news on Twitter.

Governments may have the capacity to flood social media with fake news, but little is known about the use of flooding by ordinary voters. In this work, we identify 2107 registered US voters who account for 80% of the fake news shared on Twitter during the 2020 US presidential election by an entire panel of 664,391 voters. We found that supersharers were important members of the network, reaching a sizable 5.2% of registered voters on the platform. Supersharers had a significant overrepresentation of women, older adults, and registered Republicans. Supersharers' massive volume did not seem automated but was rather generated through manual and persistent retweeting. These findings highlight a vulnerability of social media for democracy, where a small group of people distort the political reality for many.

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.

Using and Comparison of Artificial Intelligence Techniques to Detect Misinformation and Disinformation on Twitter

This research investigates diverse artificial intelligence (AI) techniques for detecting misinformation on Twitter, addressing the pervasive concern of misinformation and fake news affecting public discourse. Employing models such as Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Random Forest Classifier, Multinomial Naive Bayes and Gradient Boosting Classifier, we discern deceptive content from reliable information. Utilizing a dataset of 23,481 false tweets and approximately 21,417 real tweets, our analysis leverages Natural Language Processing (NLP), Deep Learning (DL) and Machine Learning (ML) techniques, showcasing the effectiveness of each model in identifying misinformation patterns. Our investigation rigorously assesses the strengths and limitations of AI techniques, focusing on accuracy, efficiency and scalability. Notably, the best results are achieved by models such as LSTM (98.84% accuracy, 98.79% F1 score), SVM (99.44% accuracy, 99.44% F1 score) and XGBoost Classifier (99.82% accuracy, 99.81% F1 score). The findings provide valuable insights into the performance of key models and serve as a resource for academics and researchers in the fields of artificial intelligence and social media analysis. Additionally, they provide practical guidance for supporting information integrity on Twitter, contributing to ongoing efforts to combat misinformation and enhance information credibility.

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.

Pentingnya Mewujudkan Pertahanan dan Keamanan Bagi Generasi Z di Era Media Sosial

In the digital era, the spread of hoaxes poses a serious threat to national defense and security. False and misleading information can easily spread through social media, causing unrest in society and even disrupting the stability of the country. Generation Z, as active users of social media, is vulnerable to being exposed to hoaxes and is the main target for spreading them. This research aims to socialize the importance of digital literacy and strategies for dealing with hoaxes to Generation Z at SMAN 108 Jakarta. By increasing their understanding and skills in analyzing and verifying information, it is hoped that Generation Z can become the country's defense in the digital era. This research uses a descriptive qualitative method with a case study approach. Data was collected through interviews, observations and surveys of representatives of Generation Z students at SMAN 108 Jakarta. The research results show that Generation Z has sufficient understanding of digital literacy and hoaxes. They also have sufficient skills to analyze and verify information. The dissemination of digital literacy and strategies for dealing with hoaxes has proven to be effective in comprehensive information about SMAN 108 Jakarta students' understanding of the spread of hoaxes in the digital sphere. After participating in the socialization, they showed an increase in students' understanding of SMAN 108 Jakarta in using technology as wisely as possible, as well as how to deal with the spread of fake news on social media from the socialization activities that would be carried out. This research shows that disseminating digital literacy and strategies for dealing with hoaxes are important for improving national defense and security in the digital era. Generation Z needs to become smart and responsible netizens to combat the spread of hoaxes. With adequate understanding and skills, they can become agents of change and contribute to the creation of a safe and conducive digital society. It is hoped that this socialization can provide a deep understanding of the importance of handling hoaxes that spread in the digital space and become the basis for an education project for young students at SMAN 108 Jakarta as Generation Z, thereby giving birth to a generation that can think critically. With joint efforts, we can realize stronger national defense and security in the digital era.

A Review on Fake News Detection using Deep Learning Methods

The proliferation of fake news in online media platforms poses a significant threat to the integrity of information dissemination and public discourse. In response, researchers have increasingly turned to deep learning techniques to develop effective solutions for detecting and mitigating the spread of fake news. This review paper provides a comprehensive overview of recent advances in fake news detection using deep learning methodologies. We survey the literature on various deep learning architectures and approaches employed for fake news detection, including supervised, semi-supervised, and unsupervised learning methods. We discuss the challenges associated with data preprocessing, feature extraction, and model evaluation, and examine the ethical considerations and societal implications of deploying deep learning models for fake news detection. Furthermore, we identify emerging trends and future research directions in the field, with a focus on addressing the evolving nature of fake news and enhancing the robustness and interpretability of detection systems. This review contributes to the ongoing discourse on fake news detection and provides valuable insights for researchers, practitioners, and policymakers working in the domain of information integrity and online media governance.

Improvement of a Machine Learning Model Using a Sentiment Analysis Algorithm to Detect Fake News

These days, the problem of fake news has grown to be a major social and personal concern. With the amount of information generated through social media, it is very crucial to be able to detect and properly take care of that fake information. Previous studies proposed a machine learning model to detect fake news in online Thai health and medical articles. Still, the problem of detecting fake news with similar content but different objectives exists, and the accuracy of the model needs improvement. Therefore, this study aims to solve these problems by adding 33 new features, including textual features, sentiment-based features, and lexicon features, i.e., herbs, fruits, and vegetables, to identify the objective of an article. We trained and tested the model's prediction accuracy on a new dataset containing 582 reliable and 435 unreliable (fake news) articles from eight Thai websites. Our improved classification model using XGBoost with Lasso, the best feature selection method, achieved an accuracy of 97.76% without over-fitting, reflecting a 7.16% improvement over our earlier model.

Enhancement of random forest algorithm applied in fake news detection

The widespread increase of fake news presents a serious obstacle in today's information-sharing situation, making it more difficult to distinguish fact from fabricated information. The study aims to improve machine learning algorithms' capacity to identify fake news to address this significant issue. To improve detection accuracy, Researchers applied a method that combines the Random Forest algorithm with Adaptive Boosting, or AdaBoost. By utilizing ensemble learning techniques, our method successfully leverages the collective intelligence of numerous decision trees, outperforming traditional approaches. By carefully experimenting with hyperparameters using the Random Search, The proposed method improved the algorithm's capacity to identify significant patterns suggestive of false information. The integration of AdaBoost to Random Forest produces a significant improvement, from 92.56% to 99.79% accuracy average difference, with an accuracy gain of 7% over the baseline Random Forest model. This improvement emphasizes how effective ensemble methods are at negotiating the complexities in terms of fake news detection. This study adds to the ongoing efforts to protect the integrity of information in the digital era by showcasing the efficacy of sophisticated machine-learning approaches in addressing the complex problem of fake news. The study underscores the critical importance of continuous innovation and adaptability in combating the increase of misinformation. Through interdisciplinary collaboration and technological advancements, the aim is to fortify defenses against the detrimental spread of false information, fostering an information ecosystem that prioritizes truth and resilience.

Video Detection Method Based on Temporal and Spatial Foundations for Accurate Verification of Authenticity

With the rapid development of deepfake technology, it is finding applications in virtual movie production and entertainment. However, its potential for malicious use, such as generating false information, fake news, or synthetic pornography, poses significant threats to national and social security. Various research disciplines are actively engaged in developing deepfake video detection technologies to mitigate the risks associated with malicious deepfake content. Therefore, the importance of deepfake video detection technology cannot be overemphasized. This study addresses the challenge posed by images in nonexistent datasets by analyzing deepfake video detection methods. Using temporal and spatial detection techniques and employing 68 facial landmarks for alignment and feature extraction, this research integrates the attention-guided data augmentation (AGDA) strategy to enhance generalization capabilities. The detection performance is evaluated on four datasets: UADFV, FaceForensics++, Celeb-DF, and DFDC, with superior results compared to alternative approaches. To evaluate the study’s ability to accurately discriminate authenticity, detection experiments are conducted on both genuine and deepfake videos synthesized using the DeepFaceLab and FakeApp frameworks. The experimental results show better performance in detecting deepfake videos than other methods compared.

Digital Literacy in Education: An Analysis of Critical Thinking Culture for Preventing the Hoaxes

In the 21st century, a hoax is one of the biggest problems in the world, especially in Indonesia. The post-truth phenomenon and the ease of disseminating information on social media are the primary triggers for this case. Moreover, this disruption era is marked by a flood of information, rapid change, and high use of internet technology. This research intends to analyze the importance of digital literacy, especially critical thinking (CT) culture, in preventing hoax news. A literature review is employed in this research. This method explains how CT skills can be used in digital literacy. The results underscore several points: a) the urgency of digital literacy is based on a big problem in the digital world in Indonesia, namely hoaxes; b) digital literacy can be applied in tips such as those discussed by Facione (1998) regarding the six CT skills that require technical and theoretical skills; c) emphasize technical skills in assessing account identities or media that produce information by Nagler (2017); and d) there are several opportunities for implementing digital literacy. Hence, this research concludes the importance of instilling CT skills in fighting hoax news or misinformation. Apart from that, if we look at the data regarding the low level of digital literacy in Indonesian society, it is urgent to implement digital literacy methods and CT skills integrated with educational institutions or more formal policies.

Fake News Detection

The spread of false information on the internet has become a major social issue, casting doubt on the veracity of information shared on these platforms. This study uses cutting-edge methods from machine learning (ML) and natural language processing (NLP) to present a complete framework for the detection of fake news. The purpose of this paper is to develop a model for detecting bogus news. A model is selected by using supervised learning techniques. In addition, we categorize news stories as real or fraudulent using the Naïve Bayes, Logistic Regression, and Random Forest algorithms. Our methodology offers an approach to false news identification that is more robust by taking into account the credibility of the news sources in addition to the content of the news. Using labeled datasets of fictitious and authentic news stories, we train our algorithms. A few methodologies were compared to achieve varying degrees of accuracy. When compared to the other two models, Random Forest is thought to have produced the best results in terms of accuracy. We assess our framework's effectiveness using real-world news articles and benchmark datasets, showcasing its versatility in correctly recognizing false information in a variety of settings and domains. We demonstrate the advantages of our method in terms of detection accuracy, scalability, and computational efficiency by comprehensive experimentation and comparative analysis. All things considered, our suggested framework is a major step forward in the fight against the dissemination of false information on the internet and provides a workable way to lessen the negative effects of fake news on people, communities, and society at large.

DISCOURSE: KNOWLEDGE, NEWS, AND FAKE INTERTWINED

Discourse has been a focal point for linguists over an extended period. The multidisciplinary character of the term ‘discourse’ has resulted in diverse approaches aiming to define and explore this phenomenon. The study of various facets of discourse significantly enhances its comprehension. For our understanding, discourse encompasses the following pertinent characteristics: it is a communicative process, a dialogue, an interaction between communicators; it is a structure, a system, a distinct way of representing and perceiving the world, a special language and world, an expression and manifestation of national identity. Discourse implies knowledge, as it includes various types of it. Knowledge and news are closely linked. They are processed in discourse and with the help of it. Any explicit or implicit knowledge and news influence discourse production and comprehension. True and fake information can alter existing types of knowledge and create new ones, influence mental processes, and shape new mental models. The aim of the article is to describe discourse in its close connection with its main constituents – knowledge, news and fake. ‘Fake’ becomes a linguistic and discursive matter. It is associated with the concepts of ‘lie’, ‘deception’, and ‘manipulation’. Corpus data about ‘fake’ and ‘fake news’ give a bigger picture of the overall context of their representation, changes and dynamics in the usage. The widespread influence of fake is provided with the help of mass media. Strategic control of knowledge in the interaction process is achieved through the use of knowledge itself, accordingly, it becomes possible to control knowledge by means of deception and fake. Fake news is a genre of news information and news discourse, it is an integrative type of media text. The main goal of fake news is to deceive and manipulate consciousness of a wide audience. Manipulation entails language to subtly influence recipients, encouraging them covertly to undertake particular actions. Fake news can be studied within news discourse, media discourse, television discourse, manipulative discourse, political discourse, discourse of fake, and other types. Thus, knowledge, news and fake presented in discourse and with the help of it allow to create ‘new knowledge’, shape the audience’s understanding of discourse, make and generate mental models, control and restrict access to certain information, manipulate consciousness of the audience, transform traditional mental attitudes associated with the pursuit of truth.

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

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

FNNet: a secure ensemble-based approach for fake news detection using blockchain

FNNet: a secure ensemble-based approach for fake news detection using blockchain

A Multimodal Fusion and Ensemble Approach for Robust Fake News Detection through Deep Learning, Reinforcement Learning, and Using Blockchain to Enhance Security and Authorization

Background: Computer science significantly influences modern culture, especially with the rapid breakthroughs and technology in social media networking. Social media platforms have become significant channels for sharing and exchanging daily news and information on many issues in the current digital environment, which is known for its massive data collection and transmission capabilities. While there are many benefits to this environment, there are also many false reports and information that deceive readers and users into believing they are receiving correct information. Objective: Nowadays, all users use social media to obtain news content, but sometimes some malicious users tamper with real news and then spread fake news, which may reduce the reputation of social media. Therefore, many existing models have been introduced to detect fake news, but these models are based on traditional machine learning algorithms, such as decision tree (DT), multilayer perceptron (MLP), random forest (RF), etc. These models Lack of performance, security, and authorization. Our proposed model can solve existing model problems using reinforcement learning and blockchain technology. objective: Nowadays, all users use social media to obtain news content, but sometimes some malicious users tamper with real news and then spread fake news, which may reduce the reputation of social media. Therefore, many existing models have been introduced to detect fake news, but these models are based on traditional machine learning algorithms, such as decision tree (DT), multilayer perceptron (MLP), random forest (RF), etc. These models Lack of performance, security, and authorization. Our proposed model can solve existing model problems using reinforcement learning and blockchain technology. Methods: In this research paper, we explain a new way to identify fake news. This new approach, combined with policy-based heuristic reinforcement learning (PHRL), where the model dynamically adjusts through iterative learning, is the key innovation and gradually improves classification accuracy. The same as our smart contract authorization method, which enhances the authenticity of content posted safely by authorized users and improves the transparency and accountability of information. Results: Our model was tested on real-time information collected from various sources with 70% accuracy and valid authentication. Conclusion: Our proposed model produced better results with a Mean Absolute Error (MAE) of 0.0811 and Root Mean Squared Error (RMSE) of 0.2847, both significantly lower values. Our proposed model performs better than multilayer perceptron (MLP), and random forest (RF).

Survey of machine learning techniques for Arabic fake news detection

Social media platforms have emerged as primary information sources, offering easy access to a wide audience. Consequently, a significant portion of the global population relies on these platforms for updates on current events. However, fraudulent actors exploit social networks to disseminate false information, either for financial gain or to manipulate public opinion. Recognizing the detrimental impact of fake news, researchers have turned their attention to automating its detection. In this paper, we provide a thorough review of fake news detection in Arabic, a low-resource language, to contextualize the current state of research in this domain. In our research methodology, we recall fake news terminology, provide examples for clarity, particularly in Arabic contexts, and explore its impact on public opinion. We discuss the challenges in fake news detection, outline the used datasets, and provide Arabic annotation samples for label assignment. Likewise, preprocessing steps for Arabic language nuances are highlighted. We also explore features from shared tasks and their implications. Lastly, we address open issues, proposing some future research directions like dataset improvement, feature refinement, and increased awareness to combat fake news proliferation. We contend that incorporating our perspective into the examination of fake news aspects, along with suggesting enhancements, sets this survey apart from others currently available.

Characterizing Fake News Targeting Corporations

Characterizing Fake News Targeting Corporations

A Multi-modal Prompt Learning Framework for Early Detection of Fake News

A Multi-modal Prompt Learning Framework for Early Detection of Fake News

Deceptive Content Detection Using Machine Learning

Deceptive content, such as fake news, poses a significant challenge in today's information landscape, influencing public opinion and decision-making processes. This paper presents an innovative approach for the detection of deceptive content using machine learning techniques. The proposed system leverages a combination of natural language processing and supervised learning algorithms to identify patterns indicative of misinformation in textual data Our approach leverages term frequency-inverse document frequency (TF-IDF) of bag-of-words and n-grams as feature extraction methods, complemented by the utilization of Support Vector Machine (SVM) as a classifier. Additionally, we introduce a meticulously curated dataset comprising both fake and genuine news articles to train and evaluate our proposed system. Our findings underscore the efficacy of the developed framework, demonstrating its capability in discerning between fake and authentic news articles. Keywords — Deceptive Content, TfidfVectorizer, Natural Language Processing, Text Classification, Passive-Aggressive Classifier, Machine Learning

Geo-political bias in fake news detection AI: the case of affect

AbstractThere have been massive advances in AI technologies towards addressing the contemporary challenge of fake news identification. However, these technologies, as observed widely, have not had the same kind or depth in impact across global societies. In particular, the AI scholarship in fake news detection arguably has not been as beneficial or appropriate for Global South, bringing geo-political bias into the picture. While it is often natural to think of data bias as the potential reason for geo-political bias, other factors could be much more important in being more latent, and thus less visible. In this commentary, we investigate as to how the facet of affect, comprising emotions and sentiments, could be a potent vehicle for geo-political biases in AI. We highlight, through assembling and interpreting insights from literature, the overarching neglect of affect across methods for fake news detection AI, and how this could be a potentially important factor for geo-political bias within them. This exposition, we believe, also serves as a first effort in understanding how geo-political biases work within AI pipelines beyond the data collection stage.