Fake News Detection on Datasets

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

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.

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%.

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.

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

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.

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.

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%.

Easy access to equipment, software, and platforms to create, distribute, and provide access to fake news stories has exacerbated the problem of fake news, making for a large number of highly biased sources that are reaching the mainstream through social networks. The economics of emotion theory proposes that fake news headlines are created to evoke emotional responses in readers that will cause them to interact with the article in a way that allows the creator to make a profit (through clicking on the link to the full article, by sharing the article, etc.). This mixed methods study investigates the process by which individuals experience discrete emotional reactions to fake news headlines, and how these emotions contribute to the perpetuation of fake news through sharing behaviors. U.S. participants (n=879 across two waves) viewed one of eight false news headlines and reported their emotional reactions, belief in the headline, and potential sharing behaviors. In general, participants were more likely to believe headlines that aligned with their existing beliefs (e.g., liberals were more likely to believe negative news about conservatives), reacted with more negative emotions to headlines that attacked their party, and were more likely to report intentions to suppress (e.g., post a link to a fact check) fake news that attacked their own party. Emotional reactivity of participants was associated with response behavior intentions such that participants who reported high levels of emotions were more likely to take actions that would spread or suppress the fake news, participants who reported low levels of emotions were more likely to ignore or disengage from the spread of false news, and participants who reported high levels of negative emotions and low levels of positive emotions were more likely to suppress the spread of fake news and less likely to contribute to the spread of fake news. Our findings are synthesized into a process model that explains how discrete emotions and beliefs influence sharing behaviors. Implications for mitigating the spread of fake news are discussed in terms of this model.

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.

Social media usage is growing globally, with an exponential increase in low- and middle-income countries. Social media changes the ways in which information-sharing occurs, intensifying the population's exposure to misinformation, including fake news. This has important repercussions for global health. The spread of fake news can undermine the implementation of evidence-based interventions and weaken the credibility of scientific expertise. This is particularly worrisome in countries, such as Brazil, in a sociopolitical context characterized by a lack of popular trust in public institutions. In this project report, we describe our experience with the spread of fake news through the social media platform WhatsApp during the implementation of a cluster randomized controlled trial aimed at reducing dengue incidence in children in Fortaleza (Brazil). During initial visits to selected clusters, the research team was met with resistance. Then, soon after data collection started, fake news began circulating about the study. As a result, the research team developed strategies to dispel suspicion and further promote the study. However, the climate of violence and mistrust, coupled with the COVID-19 pandemic, forced the interruption of the study in 2019. The lessons learned from our experience in Fortaleza can be useful to other researchers and practitioners implementing large-scale interventions in this era of health-related misinformation.

The expeditious growth of technology with social media as a platform for communication has led to a proliferous increase in the spread of misinformation and fake news. The ongoing COVID-19 widespread has pushed us to review posts on various social media platforms to stop people from being subjected to false and perilous posts. Detecting fake news in social media has been the need of an hour. The proposed research work has approached it with various Transformer and recurrent models with several contextual word embedding models. Furthermore, the effectiveness of the proposed model is evaluated by using a different loss function instead of the conventional loss function, Binary cross Entropy. The fake news detection is considered as a sequence classification task, one of the downstream tasks of natural language processing. It has been observed that using domain-specific language models along with custom loss function has achieved the highest weighted average F1-score.

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.

'Cure or Poison?' Identity Verification and the Spread of Fake News on Social Media

Staying up-to-date on news through social media has both advantages and disadvantages. Social media platforms are excellent sources of information, but they also contribute significantly to the spread of fake news (incorrect or poor-quality content). The widespread circulation of such information can profoundly impact society by shaping public opinion, influencing policy debates, and affecting decision-making processes. As a result, fake news detection has become a critical area of research, particularly in multilingual and multicultural contexts. Detecting fake news in Arabic presents unique challenges due to the language’s complexity, diverse dialects, and the limited availability of comprehensive annotated datasets. Many existing detection models are vulnerable to misleading or context-heavy information because they rely primarily on basic content features. This study evaluates the effectiveness of various machine learning and deep learning techniques—including random forest (RF), naive Bayes (NB), feedforward neural networks (FNN), long short-term memory (LSTM), Arabic bidirectional encoder representations from Transformers (BERT), and a hybrid AraBERT + bidirectional long short-term memory (BiLSTM) model—in identifying fake news in Arabic media. Experiments were conducted using the Arabic Fake News Dataset (AFND), which contains over 606,000 news items labeled according to credibility. The hybrid AraBERT + Bi-LSTM model outperformed all others, achieving an average F1-score of 98.0%. This model integrates transformer-based contextual embeddings with sequential modeling.