A Novel Score-Based Multi-Source Fake News Detection using Gradient Boosting Algorithm

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.

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

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.

The rise of fake news has become a significant global concern, undermining public trust and information integrity. This study explores the application of advanced machine learning algorithms for detecting fake news, leveraging a balanced dataset of real and fake news articles. Through rigorous preprocessing, including text cleaning and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, the study enhances data quality and model performance. Five machine learning models—Random Forest, Support Vector Machine (SVM), Neural Networks, Logistic Regression, and Naïve Bayes—are systematically evaluated using metrics such as accuracy, precision, recall, and F1-score. Results indicate that the Random Forest Classifier outperforms other models with an accuracy of 99.95% and balanced performance across metrics, demonstrating its robustness in distinguishing fake from real news. SVM and Neural Networks also achieve high accuracy, showcasing their capability in handling complex data. Logistic Regression and Naïve Bayes, while computationally efficient, exhibit relatively lower performance. The findings underscore the importance of ensemble methods and sophisticated preprocessing techniques in detecting fake news effectively. This research provides a methodological framework for scalable fake news detection, offering valuable insights for developing automated systems to combat misinformation and promote informed decision-making in the digital age.

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 phenomenon of Fake news is experiencing a rapid and growing progress with the evolution of the means of communication and Social media. Fake news detection is an emerging research area which is gaining big interest. It faces however some challenges due to the limited resources such as datasets and processing and analysing techniques. In this work, we propose a system for Fake news detection that uses machine learning techniques. We used term frequency-inverse document frequency (TF-IDF) of bag of words and n-grams as feature extraction technique, and Support Vector Machine (SVM) as a classifier. We propose also a dataset of fake and true news to train the proposed system. Obtained results show the efficiency of the system. In this work, we propose a system for Fake news detection that uses machine learning techniques. We used term frequency-inverse document frequency (TF-IDF) of bag of words and n-grams as feature extraction technique, and Support Vector Machine (SVM) as a classifier. We propose also a dataset of fake and true news to train the proposed system. Obtained results show the efficiency of the system.

SummaryIn the present epoch of computing, the world has changed from older conventional print media to social platform channels. Fake news articles have the prospects to handle the opinions of the public and so may harm human groupings. Therefore, it is necessary to explore the authenticity and credibility of the news flash being shared on the internet community. Hence, this research paper devises an efficient and robust fake news detection model, named Exponential Chimp Optimization Algorithm (EChOA)‐based Deep Neuro‐Fuzzy Network (DNFN) for detecting fake news. The introduced model utilizes a MapReduce framework that includes the mapper and reducer phases for processing big data for detecting fake news. First phase of processing is the Mapper work, in which every input used in the database is processed and creates an intermediate key‐value pair. In the reducer phase, the fusion of features is performed by arranging the features with the help of computing the optimal parameter and Rand similarity coefficient using a Deep Q Network (DQN). Here, the detection of fake news is obtained by DNFN, and the DNFN is done using implemented EChOA. The EChOA‐based DNFN effectively generates robust and effective fake news detection performance by choosing the optimal feature subsets through feature fusion. The EChOA is designed by integrating the Exponential Weighted Moving Average (EWMA) and Chimp Optimization Algorithm (ChOA). Moreover, the EChOA‐based DNFN method outperformed various former fake news detection approaches and attains the highest performance based on the testing accuracy is 0.909, sensitivity is 0.937, and specificity is 0.891 using the FakeNewsNet dataset.

The exponential growth of digital media and social networking platforms has transformed the way information is created and consumed. While these platforms provide rapid access to news, they have also facilitated the widespread dissemination of fake news. Fake news refers to intentionally false or misleading information presented as authentic news, which can influence public opinion, disrupt social harmony, and erode trust in media. Traditional manual fact-checking approaches are time-consuming and inadequate for handling the massive volume of online content. Therefore, automated fake news detection systems are essential. This paper proposes a machine learning-based approach for detecting fake news using Natural Language Processing (NLP) techniques. The proposed system pre-processes textual data and applies feature extraction methods such as Bag of Words (BoW) and Term Frequency–Inverse Document Frequency (TF-IDF). Multiple machine learning classifiers, including Logistic Regression, Naïve Bayes, Random Forest, and Support Vector Machine, are trained and evaluated. The performance of these models is assessed using accuracy, precision, recall, and F1-score. Experimental results demonstrate that classical machine learning models can effectively classify news articles as fake or real with reliable accuracy. The proposed system offers a lightweight and efficient solution for fake news detection and can be extended to real-time applications in the future.

Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community’s behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency–inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques.

In recent years widespread rumors and fake news has given rise to many social and political problems. Most of the information today is acquired from digital sources. In Digital media it is difficult to assign accountability to the opinion due to which the data received cannot be authenticated. Lack of constant supervision has motivated the miscreants to spread fake information. Fake news articles that are planted over digital media shares important linguistic features such as immoderate usage of unconfirmed hyperbole and non-verified quotes. It is necessary to invent an automated mechanism to identify fake news and also to minimize its impact by restricting its spread. This survey comprehensively and systematically studies different methodologies in the detection of fake news in digital media. The survey identifies and specifies fundamental theories in Machine Learning, to facilitate and enhance the research of fake news detection. By understanding the different methodologies in fake news studies, we highlight some potential research gaps at the end of this survey.

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.

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.

Fake news is a real problem in today’s world, and it has become more extensive and harder to identify. A major challenge in fake news detection is to detect it in the early phase. Another challenge in fake news detection is the unavailability or the shortage of labelled data for training the detection models. We propose a novel fake news detection framework that can address these challenges. Our proposed framework exploits the information from the news articles and the social contexts to detect fake news. The proposed model is based on a Transformer architecture, which has two parts: the encoder part to learn useful representations from the fake news data and the decoder part that predicts the future behaviour based on past observations. We also incorporate many features from the news content and social contexts into our model to help us classify the news better. In addition, we propose an effective labelling technique to address the label shortage problem. Experimental results on real-world data show that our model can detect fake news with higher accuracy within a few minutes after it propagates (early detection) than the baselines.

This paper draws together insights from a variety of fields (including philosophy, psychology, information studies, sociology, politics, and media studies) to synthesize insight into why fake news is created, disseminated, sustained and authorized so as to understand how and why it is successful and how it might be challenged. The premier case for analysis will be Trump, his supporters, his party and his media. Central to this issue is the role of cognitive authorities, a notion first articulated and developed by Patrick Wilson (1983). Honest cognitive authorities have credibility and expertise and are regarded as trustworthy. Their knowledge, based on direct and verifiable knowledge, is sought, communicated and accepted, when an information seeker comes to them about a matter of which an information seeker has come to believe that they have expertise, credibility and knowledge. Pseudo- or false cognitive authorities appear to have the same qualities of credibility, expertise and trustworthiness, but on critical examination they fail in these qualities and strive to impose a partisan agenda irrespective of truth, evidence, logic or facts. Unfortunately, these conditions do not deter believers from accepting them. These authorities are of various types, such news programs or organizations, religious leaders, or social media sites, that create, propagate, authorize and legitimatize fake news stories, that partisan adherents are willing to accept and perpetuate through a form of collective self-deception and who will at the same time denigrate sources and cognitive authorities of genuine and verified information or knowledge. Starting with the InfoWars, we proceed to discuss the nature of the forms of false information on the internet, and the role of deception, particularly self-deception, social self-deception, and collective self-deception in the acceptance real fake news, which is authorized and legitimatized by pseudo-cognitive authorities. In the process we contrast genuine cognitive authorities with dishonest ones, and show how the psychological factors, motivations, and collective self-deception feed each other into a reinforcing collective self-deception so strong it may be equivalent to a cult. This dialogical process (pseudo-cognitive authorities deceiving and self-deceiving themselves and their listeners, who in turn “validate” those authorities through word-of-mouth and seeking and associating with like-minded groups) is reinforced by repetition, the Dunning-Kruger effect, agnotology, and other factors. At the conclusion the roles of information professionals will be examined concerning the difficulties confronting fake news and fake news adherents and developing paths for successful strategies in coping with them.

With the extensive technological advancements and expansion, the persistent issues regarding the creation and rapid dissemination of fake news have become a prevalent and recurrent concern. The manipulation of news content has critical repercussions, such as causing public mistrust, fear, harm, and misinformation. Addressing that, this study developed a supervised machine learning algorithm that can accurately classify social media data as fake news. The methodology of the proposed fake news detection model involved five main components: data acquisition from Twitter, data preprocessing, data transformation, model development using Naïve Bayes, decision tree, and support vector machine (SVM) and model evaluation using accuracy, precision, recall and F1-score. The results revealed that decision tree recorded the highest accuracy for both textual data (100%) and metadata (94.54%) and consistently outperformed both Naïve Bayes and SVM in terms of precision, recall, and F1-score metrics, with a score of 100% for the classification of textual data-based datasets. Regarding the metadata-based classification, decision tree also demonstrated excellent performance, with the highest F1-score of 94% for fake news data. Meanwhile, SVM exhibited the highest precision and recall performance for the metadata-based classification. Overall, the application of the decision tree classifier was deemed the most effective in Twitter fake news detection.