Multi-view co-attention network for fake news detection by modeling topic-specific user and news source credibility

Nowadays, with the advancement of technology, the use of news sources has also undergone a great evolution. News sources have constantly evolved from past to present, ranging from magazines to radios, from newspapers to televisions. The fact that it has become so easy to access news has caused society to pay more attention to fake news. Fake news has the ability to influence society through news sources such as social media, which can reach wider audiences with the development of technology. The difficulties of users in accessing accurate and reliable sources in this information flow that shapes their daily lives increases the potential for the spread of fake news, and it becomes increasingly difficult to distinguish between real and fake news. In this study, classification models for fake news detection were designed using machine learning algorithms. The dataset, which includes fake and real news examples, contains 42,000 examples. Each class, including fake and real samples, contains 22,000 sample data. In order to increase data quality, accuracy and usability, preprocessing methods were applied to the data set. The removal of numbers, stop words, and html tags was done in the pre-processing step to remove unnecessary information from the text. Models were created for fake news detection with singular and ensemble classification algorithms. Performance evaluation of the models was performed using 5-fold cross-validation. In the performance comparisons of the models, values such as accuracy, sensitivity, specificity, tp rate and fp rate were calculated. The highest performance results were observed in the random forest classification algorithm with an accuracy rate of 76%.

The dissemination of fake news in an online social network platform has been a real concern nowadays. Through social media, news articles are posted by many sources like news channels, websites, or even newspaper websites. There is a need to be sure that the information posted is only from credible sources and these posts have to authenticate. The intensity of genuineness of the news posted online cannot be measured absolutely and is still challenging. A novel Score -based Multi-Source Fake News Detection framework is proposed in this work to automate the detection of fake news from multiple news sources. This framework extracts the text-based features from genuine and fake news articles using Term Frequency - Inverted Document Frequency. Then the credibility score of the source s is calculated based on the site_url features and Top Level Domain. By assimilating the text-based features with the credibility score of multi-source, the credibility of the news is estimated. The proposed framework is applied to the Machine Learning (ML) classifiers to examine their performance in the detection of fake news. The experimental results determine the efficacy of the proposed framework with the Gradient Boosting algorithm of about 99.5% to the utmost level.

Research background: Social media plays a crucial role today in enhancing or limiting how fake news is spread. Whether devised by man or developed by artificial intelligence, it has the power to rapidly change consumers’ minds, encouraging them to adopt new behaviors, perceive situations differently, or even act in total opposition to what might be expected. The new dynamics of communication highlights the need for an organizational response adapted to new AI technologies and to the dissemination of fake news within social media networks. Purpose of this article: This paper aims to reveal, by means of bibliometric analysis and a systematic literature review, the generative capabilities of artificial intelligence in the creation and spread of fake news in the business environment, acknowledging the role of previous research in predicting accurately the constant developments in contemporary society. Methods: The analysis is based on a PRISMA flowchart to examine how artificial intelligence technologies contribute to the creation of fake news whilst also highlighting potential artificial intelligence regulations and standards for limiting the dissemination of false information. Initially, the database included over 3,400 highly cited articles retrieved from Scopus and Web of Science, published in the last years, from which a total of 203 were selected for inclusion in the analysis. The bibliometric analysis follows research directions related to detection methods and strategies, legislation and policies governing artificial intelligence technologies used in the creation and dissemination of fake news connected to the business environment. Fake news typologies relating to the advancement of artificial intelligence new technologies are also explored. Findings & value added: By analysing important phrases, including false information, misinformation, disinformation, mal-information, and deepfakes, this research investigates the categorization of fake news linked to the business environment and social media concepts. It underscores the need for better truth comprehension and the significance of fact-checking in preventing the spread of false information, with governance and institutional implications in terms of the economics of artificial intelligence-generated fake news in the social media market. While previous studies have examined the fake news phenomenon from several angles, there is still a research gap, as the literature concentrates more on how fake news is consumed rather than how it is created. This research aims to bridge the gap by providing a comprehensive examination of fake news research from the perspectives of fake news typology, creation, detection, and regulatory means.

Fake news is the kind of news that is made deliberately to deceive the readers. It is a sort of purposeful publicity which is distributed as veritable information. Fake news is spread out through traditional news media and social media. Fake news has been an issue for quite a while. With the introduction of social media, the spread of fake news is increased, and it got hard to separate between true news and fake news. The spread of fake news involves worry as it manipulates public opinions. The widespread of fake news can adversely affect people and society in general. The problem of fake news has increased significantly in recent years. The scope and impact of social media have shifted dramatically. On the one hand, it is low cost and simple accessibility, as well as the ability to quickly exchange information attract people to read news from it. On the other hand, it empowers widespread fake news, which is only false data to deceive people. A stand-alone bidirectional encoder representations from transformers model is used in this paper for fake news detection. We try to pose the problem as a text classification problem and build a deep learning model for achieving the objective. We used the BERT model to develop fake news or real news classification solution for our solution in this model. We achieved more than 95% accuracy on the test set and a remarkable by the area under the curve (AUC) by a stand-alone BERT model.KeywordsNews detectionBERTDeep learningMachine learningSocial mediaTransformerNLP

Fake news can have a significant negative impact on society because of the growing use of mobile devices and the worldwide increase in Internet access. It is therefore essential to develop a simple mathematical model to understand the online dissemination of fake news. In this study, we propose a point process model of the spread of fake news on Twitter. The proposed model describes the spread of a fake news item as a two-stage process: initially, fake news spreads as a piece of ordinary news; then, when most users start recognizing the falsity of the news item, that itself spreads as another news story. We validate this model using two datasets of fake news items spread on Twitter. We show that the proposed model is superior to the current state-of-the-art methods in accurately predicting the evolution of the spread of a fake news item. Moreover, a text analysis suggests that our model appropriately infers the correction time, i.e., the moment when Twitter users start realizing the falsity of the news item. The proposed model contributes to understanding the dynamics of the spread of fake news on social media. Its ability to extract a compact representation of the spreading pattern could be useful in the detection and mitigation of fake news.

The online spread of fake news on various platforms has emerged as a significant concern, posing threats to public opinion, political stability, and the dissemination of reliable information. Researchers have turned to advanced technologies, including machine learning (ML) and deep learning (DL) techniques, to detect and classify fake news to address this issue. This research study explores fake news classification using diverse ML and DL approaches. We utilized a well-known “Fake News” dataset sourced from Kaggle, encompassing a labelled news collection. We implemented diverse ML models, including multinomial naïve bayes (MNB), gaussian naïve bayes (GNB), Bernoulli naïve Bayes (BNB), logistic regression (LR), and passive aggressive classifier (PAC). Additionally, we explored DL models, such as long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM. We compared the performance of these models based on key evaluation metrics, such as accuracy, precision, recall, and the F1 score. Additionally, we conducted cross-validation and hyperparameter tuning to ensure optimal performance. The results provide valuable insights into the strengths and weaknesses of each model in classifying fake news. We observed that DL models, particularly LSTM and CNN-LSTM, showed better performance compared to traditional ML models. These models achieved higher accuracy and demonstrated robustness in classification tasks. These findings emphasize the potential of DL models to tackle the spread of fake news effectively and highlight the importance of utilizing advanced techniques to address this challenging problem.

Filtering, vetting, and verifying digital information is an area of core interest in information science. Online fake news is a specific type of digital misinformation that poses serious threats to democratic institutions, misguides the public, and can lead to radicalization and violence. Hence, fake news detection is an important problem for information science research. While there have been multiple attempts to identify fake news, most of such efforts have focused on a single modality (e.g., only text‐based or only visual features). However, news articles are increasingly framed as multimodal news stories, and hence, in this work, we propose a multimodal approach combining text and visual analysis of online news stories to automatically detect fake news. Drawing on key theories of information processing and presentation, we identify multiple text and visual features that are associated with fake or credible news articles. We then perform a predictive analysis to detect features most strongly associated with fake news. Next, we combine these features in predictive models using multiple machine‐learning techniques. The experimental results indicate that a multimodal approach outperforms single‐modality approaches, allowing for better fake news detection.

With the exponential growth of user-generated content on social media platforms, the spread of fake news has become a critical challenge, undermining public trust and influencing societal behavior. This research focuses on fake news detection and knowledge verification using Deep Attention Networks, which can capture fine-grained semantic relationships and contextual dependencies in textual data. To achieve realistic and comprehensive evaluation, datasets are collected from diverse social media platforms including Twitter, Facebook, and Reddit, encompassing both verified and misleading content. The proposed methodology involves the implementation of a Deep Attentionbased model and its hierarchical comparison with existing state-of-the-art models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bi-LSTM, and Transformerbased architectures (e.g., BERT). Each model is evaluated using multiple performance metrics -accuracy, precision, recall, F1-score, and AUC - under various conditions tailored to different social media dynamics. A detailed analysis is presented through graphical representations such as bar charts, ROC curves, and confusion matrices, offering a visual and quantitative understanding of model effectiveness. The results consistently show that the Deep Attention Network provides superior performance in identifying and verifying fake news, especially in linguistically complex and context-rich scenarios. This research underscores the significance of attention mechanisms in enhancing the reliability and interpretability of fake news detection systems and lays the foundation for more robust content verification strategies on digital platforms.

Nowadays, the dissemination of news information has become more rapid, liberal, and open to the public. People can find what they want to know more and more easily from a variety of sources, including traditional news outlets and new social media platforms. However, at a time when our lives are glutted with all kinds of news, we cannot help but doubt the veracity and legitimacy of these news sources; meanwhile, we also need to guard against the possible impact of various forms of fake news. To combat the spread of misinformation, more and more researchers have turned to natural language processing (NLP) approaches for effective fake news detection. However, in the face of increasingly serious fake news events, existing detection methods still need to be continuously improved. This study proposes a modified proof-of-concept model named NER-SA, which integrates natural language processing (NLP) and named entity recognition (NER) to conduct the in-domain and cross-domain analysis of fake news detection with the existing three datasets simultaneously. The named entities associated with any particular news event exist in a finite and available evidence pool. Therefore, entities must be mentioned and recognized in this entity bank in any authentic news articles. A piece of fake news inevitably includes only some entitlements in the entity bank. The false information is deliberately fabricated with fictitious, imaginary, and even unreasonable sentences and content. As a result, there must be differences in statements, writing logic, and style between legitimate news and fake news, meaning that it is possible to successfully detect fake news. We developed a mathematical model and used the simulated annealing algorithm to find the optimal legitimate area. Comparing the detection performance of the NER-SA model with current state-of-the-art models proposed in other studies, we found that the NER-SA model indeed has superior performance in detecting fake news. For in-domain analysis, the accuracy increased by an average of 8.94% on the LIAR dataset and 19.36% on the fake or real news dataset, while the F1-score increased by an average of 24.04% on the LIAR dataset and 19.36% on the fake or real news dataset. In cross-domain analysis, the accuracy and F1-score for the NER-SA model increased by an average of 28.51% and 24.54%, respectively, across six domains in the FakeNews AMT dataset. The findings and implications of this study are further discussed with regard to their significance for improving accuracy, understanding context, and addressing adversarial attacks. The development of stylometric detection based on NLP approaches using NER techniques can improve the effectiveness and applicability of fake news detection.

This study sought to investigate factors causing the spread of fake news on digital media (DM) and to explore the sometimes disastrous consequences of fake news on social media. The study also aimed to construct a framework for fake news disaster management to control the dangers of false news on DM. The study applied PRISMA guidelines and techniques for exploring, devising, and inclusion and exclusion criteria. The search was carried out through 15 of the world’s leading digital databases. As a result, 31 peer-reviewed studies published in impact-factor journals of leading databases were included. Findings showed that several factors influenced the sharing of fake news on digital media (DM) platforms. Six major trending factors were the rise of technologies, social connections, political reasons, the absence of a controlling center, online business and marketing, and quick dissemination of information. The study identified the disadvantages of fake news (FN) on digital media (DM). A framework was constructed for managing fake news disasters to control the spread of fake news on digital media. This paper offers important theoretical contributions through the development of a framework for controlling fake news spread on digital media and by providing a valuable addition to the existing body of knowledge. The study offers practical assistance to top management, decision makers, and policymakers to devise policies to effectively manage problems caused by fake news dissemination. It provides practical strategies to address fake news disasters on digital media for redefining social values. This research also assists digital media managers in utilizing the proposed framework and controlling the harmful impact of fake news on social media.

The evolution of electronic media is a mixed blessing. Due to the easy access, low cost, and faster reach of the information, people search out and devour news from online social networks. In contrast, the increasing acceptance of social media reporting leads to the spread of fake news. This is a minacious problem that causes disputes and endangers the societal stability and harmony. Fake news spread has gained attention from researchers due to its vicious nature. proliferation of misinformation in all media, from the internet to cable news, paid advertising and local news outlets, has made it essential for people to identify the misinformation and sort through the facts. Researchers are trying to analyze the credibility of information and curtail false information on such platforms. Credibility is the believability of the piece of information at hand. Analyzing the credibility of fake news is challenging due to the intent of its creation and the polychromatic nature of the news. In this work, we propose a model for detecting fake news. Our method investigates the content of the news at the early stage i.e., when the news is published but is yet to be disseminated through social media. Our work interprets the content with automatic feature extraction and the relevance of the text pieces. In summary, we introduce stance as one of the features along with the content of the article and employ the pre-trained contextualized word embeddings BERT to obtain the state-of-art results for fake news detection. The experiment conducted on the real-world dataset indicates that our model outperforms the previous work and enables fake news detection with an accuracy of 95.32%.

PurposeThe creation and dissemination of fake news can have severe consequences for a company’s brand. Researchers, policymakers and practitioners are eagerly searching for solutions to get us out of the “fake news crisis”. Here, one approach is to use automated tools, such as artificial intelligence (AI) algorithms, to support managers in identifying fake news. The study in this paper demonstrates how AI with its ability to analyze vast amounts of unstructured data, can help us tell apart fake and real news content. Using an AI application, this study examines if and how the emotional appeal, i.e., sentiment valence and strength of specific emotions, in fake news content differs from that in real news content. This is important to understand, as messages with a strong emotional appeal can influence how content is consumed, processed and shared by consumers.Design/methodology/approachThe study analyzes a data set of 150 real and fake news articles using an AI application, to test for differences in the emotional appeal in the titles and the text body between fake news and real news content.FindingsThe results suggest that titles are a strong differentiator on emotions between fake and real news and that fake news titles are substantially more negative than real news titles. In addition, the results reveal that the text body of fake news is substantially higher in displaying specific negative emotions, such as disgust and anger, and lower in displaying positive emotions, such as joy.Originality/valueThis is the first empirical study that examines the emotional appeal of fake and real news content with respect to the prevalence and strength of specific emotion dimensions, thus adding to the literature on fake news identification and marketing communications. In addition, this paper provides marketing communications professionals with a practical approach to identify fake news using AI.

Traditional media is no more the only source of gaining news. Social media has taken over traditional media for various reasons, like being easy to use, free of cost, and always available. However, not all news published on social media is genuine, as it can come from unverified sources also. Most people believe what they read, and hence it is essential to check the authentication of news readers. The spread of fake news could cause severe impacts not just politically but also socially. We strongly believe that spread of intentionally created fake news is more harmful than an accidental one. This paper mainly focuses on the deliberate creation and sharing of false manipulated information intended to deceive and mislead audiences. Fake news detection is a classic classification problem though many attempts of solving it through clustering are being made. Our study mainly focuses on classification algorithms. We used various classification algorithms like SVM, DT, LR, and passive Aggressive Classifier on datasets to find if the news is real or fake on two different datasets. Evaluation parameters were executed to compare the efficiency of the various algorithm. Later we also try to find out which other social media the author accounts for to stop the spread of fake news.

The viral spread of fake news on social media platforms has become a significant concern. Detecting and mitigating the spread of fake news pose a major challenge, requiring extensive effort from fact-checkers, governments, and organizations. However, the speed and volume of disinformation on social media reach far beyond the capacity of manual efforts to prebunk it. In recent years, various automatic fake news detection mechanisms have been proposed, ranging from models focusing on linguistic cues to approaches analyzing network characteristics. While these methods produce successful results, ever-evolving techniques of deception often outpace them. AI-based approaches offer a new opportunity in the subject matter. Especially deep learning-based detection systems provide substantial new ways to understand the context while adapting well to the evolving patterns of fake news. In this study, we explore the evolution of fake news detection. We discuss conventional and automatic fake news detection methods and the state-of-the-art deep learning approaches.

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.