External information-augmented contrastive learning framework for fake news detection

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 proliferation of fake news across multiple modalities has emerged as a critical challenge in the modern information landscape, necessitating advanced detection methods. This study proposes a comprehensive framework for fake news detection integrating text, images, and videos using machine learning and deep learning techniques. The research employs a dual-phased methodology, first analyzing textual data using various classifiers, then developing a multimodal approach combining BERT for text analysis and a modified CNN for visual data. Experiments on the ISOT fake news dataset and MediaEval 2016 image verification corpus demonstrate the effectiveness of the proposed models. For textual data, the Random Forest classifier achieved 99% accuracy, outperforming other algorithms. The multimodal approach showed superior performance compared to baseline models, with a 3.1% accuracy improvement over existing multimodal techniques. This research contributes to the ongoing efforts to combat misinformation by providing a robust, adaptable framework for detecting fake news across different media formats, addressing the complexities of modern information dissemination and manipulation.

The uncontrolled growth of fake news creation and dissemination we observed in recent years causes continuous threats to democracy, justice, and public trust. This problem has significantly driven the effort of both academia and industries for developing more accurate fake news detection strategies. Early detection of fake news is crucial, however the availability of information about news propagation is limited. Moreover, it has been shown that people tend to believe more fake news due to their features [11]. In this paper, we present our framework for fake news detection and we discuss in detail a solution based on deep learning methodologies we implemented by leveraging Google Bert features. Our experiments conducted on two well-known and widely used real-world datasets suggest that our method can outperform the state-of-the-art approaches and allows fake news accurate detection, even in the case of limited content information.

Fake news detection is an essential task; however, the complexity of several languages makes fake news detection challenging. It requires drawing many conclusions about the numerous people involved to comprehend the logic behind some fake stories. Existing works cannot collect more semantic and contextual characteristics from documents in a particular multilingual text corpus. To bridge these challenges and deal with multilingual fake news detection, we present a semantic approach to the identification of fake news based on relational variables like sentiment, entities, or facts that may be directly derived from the text. Our model outperformed the state-of-the-art methods by approximately 3.97% for English to English, 1.41% for English to Hindi, 5.47% for English to Indonesian, 2.18% for English to Swahili, and 2.88% for English to Vietnamese language reviews on TALLIP fake news dataset. To the best of our knowledge, our paper is the first study that uses a capsule neural network for multilingual fake news detection.

With the rapid development of the Internet, the information dissemination paradigm has changed and the efficiency has been improved greatly. While this also brings the quick spread of fake news and leads to negative impacts on cyberspace. Currently, the information presentation formats have evolved gradually, with the news formats shifting from texts to multimodal contents. As a result, detecting multimodal fake news has become one of the research hotspots. However, multimodal fake news detection research field still faces two main challenges: the inability to fully and effectively utilize multimodal information for detection, and the low credibility or static nature of the introduced external information, which limits dynamic updates. To bridge the gaps, we propose ERIC-FND, an external reliable information-enhanced multimodal contrastive learning framework for fake news detection. ERIC-FND strengthens the representation of news contents by entity-enriched external information enhancement method. It also enriches the multimodal news information via multimodal semantic interaction method where the multimodal constrative learning is employed to make different modality representations learn from each other. Moreover, an adaptive fusion method is taken to integrate the news representations from different dimensions for the eventual classification. Experiments are done on two commonly used datasets in different languages, X (Twitter) and Weibo. Experiment results demonstrate that our proposed model ERIC-FND outperforms existing state-of-the-art fake news detection methods under the same settings.

Nowadays, as human activities are shifting to social media, fake news detection has been a crucial problem. Existing methods ignore the classification difference in online news and cannot take full advantage of multi-classification knowledges. For example, when coping with a post “A mouse is frightened by a cat,” a model that learns “computer” knowledge tends to misunderstand “mouse” and give a fake label, but a model that learns “animal” knowledge tends to give a true label. Therefore, this research proposes a multi-classification division-aggregation framework to detect fake news, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CKA</i> , which innovatively learns classification knowledges during training stages and aggregates them during prediction stages. It consists of three main components: a news characterizer, an ensemble coordinator, and a truth predictor. The news characterizer is responsible for extracting news features and obtaining news classifications. Cooperating with the news characterizer, the ensemble coordinator generates classification-specifical models for the maximum reservation of classification knowledges during the training stage, where each classification-specifical model maximizes the detection performance of fake news on corresponding news classifications. Further, to aggregate the classification knowledges during the prediction stage, the truth predictor uses the truth discovery technology to aggregate the predictions from different classification-specifical models based on reliability evaluation of classification-specifical models. Extensive experiments prove that our proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CKA</i> outperforms state-of-the-art baselines in 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.

In this manuscript, the rise of misinformation and harmful behavior on social media platforms has made it crucial to develop systems that ensure the authenticity of shared content and user accounts is presented. Parsing of social media feeds for forensic investigation aims to identify fake news and detect fake social media accounts (focusing on platforms like Instagram). The main goal of parsing of social media feeds for forensic investigation is to protect users from misleading information spread by fake accounts. To achieve this, various modules are integrated into the system, including Data Collection and Pre-Processing, Fake News Detection and Account Authenticity Verification. Web scraping tools such as BeautifulSoup are used to gather real-time data, while machine learning algorithms, including Random Forest and Large Language Model (LLM) are employed to classify fake news and detect suspicious account behaviors. The system also uses NLP models to identify fake news patterns and generate comprehensive reports summarizing the findings of each module. By combining Natural Language Processing (NLP) and machine learning techniques, parsing of social media feeds for forensic investigation provides an essential platform for users to protect themselves from misleading information on social networks.

The proliferation of fake news is one of the major problems that causes personal and societal harm. In today’s fast-paced digital age, misinformation spreads rapidly, often leaving individuals without the time to verify the authenticity of the information. This can cause irreparable damage to personal reputations and organizational credibility. Thus, instigated by the quintessential necessity, there is a dire need to construct a framework for the automatic detection and identification of fake news at its inception. This research presents a novel approach that leverages a combination of three popular word embeddings (FastText, FastText-Subword, and GloVe) integrated with a customized convolutional neural-network(CNN) to classify fake news accurately. The proposed model was tested against the Fake News Challenge dataset. Hundreds of word vector features were generated from the combined embedding and then managed with PCA and significant features were extracted. The proposed model gives an accuracy of 94.58%, 95.35% precision, 97.29% recall, and an F1 score of 96.11%. The proposed framework’s robustness is demonstrated when compared with other machine, deep, and ensemble learning approaches, showing superior performance. Furthermore, the effectiveness of the model is validated on an independent Arabic Fake News dataset.

In the extensive growth of social media platforms, fake news is false information spread among people around the world. Fake news weakens public trust and societal stability; it has negative impacts that necessitate for fake news. In order to enhance the public trust and improve the social media users, fake news detection is crucial, which is a complex and multi-dimensional task due to the diverse characteristics of fake news. More researchers have explored several deep learning and machine learning approaches for fake news detection approaches yet the existing binary classification mechanism is not sufficient for handling imbalanced training data to enhance poor performance in the fake news detection process also it affects the interpretability and consumes high computational duration. With the aim of rectifying these complexities, an automated fake news detection mechanism is proposed in this research work to ultimately recognise unreliable information for improving social media users’ effectiveness. Initially, the necessary data are taken from the publicly available datasets, such as the WELFake dataset and Fake News dataset; it is fed into the preprocessing phase. Here, the punctuation, special characters, redundant content and inappropriate data are mitigated, which can generate cleaned data. Then, the preprocessed data is given to the feature extraction process to efficiently extract the relevant informative features for enhancing better detection outcomes. Here, the Bidirectional Encoder Representations from Transformers (BERT) is initially used to retrieve the first set of features, the Recurrent Neural Network (RNN) extracts the temporal features and the Convolution Neural Network (CNN) is used to extract the spatial features. Subsequently, these features are fused in the weighted feature selection process, where the weights are quickly tuned through the Improved Horse Herd Optimisation Algorithm (IHHOA). Further, it is passed to the Residual Convolution Bidirectional Long Short Term Memory (RConv-Bi-LSTM) technique for significantly detecting fake news in a limited duration. The entire detection process is estimated with several performance metrics and contrasted over various conventional methods to generate optimal outcomes. Here, the designed framework’s FM value (dataset 1) is enhanced over 22.9% DNN, 8.7% CNN, 17.6% LSTM and 7% Bi-LSTM methods (BERT features) to show its better performance over the existing approaches.

The increasing prevalence of fake news on social media has become a significant challenge in today’s digital landscape. This paper proposes a hybrid framework for fake news detection, combining Natural Language Processing (NLP) techniques and machine learning algorithms. Using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction, and classifiers such as Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machines (SVM), the model integrates Maximum Likelihood Estimation (MLE) with Logistic Regression to achieve 95% accuracy and 92% precision on a Kaggle dataset. The results highlight the potential of combining statistical and NLP approaches to improve fake news detection accuracy.

The rapid development of Internet technology and the rise of social media have promoted the spread of fake news, most of which contain both text and image content. However, multimodal methods will inevitably encounter the challenge of semantic gap when analyzing these news. To solve this problem, we propose F3ND, a tri-modal integrated self-attention framework for fake news detection. F3ND combines unimodal text and image features with fused multimodal features. The fused features act as a bridge to enhance the correlation between the two unimodal features, which effectively solves the semantic gap problem when understanding multimodal content. At the same time, we introduce a self-attention mechanism to dynamically assign weights to different features, retaining the discriminative information in unimodal features that helps to determine whether the news is fake. Our experiments on Weibo and Weibo21 datasets show that F3ND can achieve better performance than many previous baseline models, proving the robustness and effectiveness of our method.

Few-Shot Fake News Detection (FS-FND) aims to distinguish inaccurate news from real ones in extremely lowresource scenarios. This task has garnered increased attention due to the widespread dissemination and harmful impact of fake news on social media. Large Language Models (LLMs) have demonstrated competitive performance with the help of their rich prior knowledge and excellent in-context learning abilities. However, existing methods face significant limitations, such as the Understanding Ambiguity and Information Scarcity, which significantly undermine the potential of LLMs. To address these shortcomings, we propose a Dual-perspective Knowledge-guided Fake News Detection (DKFND) model, designed to enhance LLMs from both inside and outside perspectives. Specifically, DKFND first identifies the knowledge concepts of each news article through a Detection Module. Subsequently, DKFND creatively designs an Investigation Module to retrieve inside and outside valuable information concerning to the current news, followed by another Judge Module to evaluate the relevance and confidence of them. Finally, a Determination Module further derives two respective predictions and obtain the final result. Extensive experiments on two public datasets show the efficacy of our proposed method, particularly in low-resource settings.

In this research, a machine learning–based framework for fake news detection using the DistilBERT-base-multilingual-cased Transformer model is proposed. The rapid growth of social media platforms has significantly increased the spread of misinformation, making accurate and scalable fake news detection a critical challenge. To address this issue, the proposed system focuses on multilingual text classification while maintaining low computational complexity, making it suitable for real-world applications. A large and diverse training dataset is created by merging multiple open-source datasets, enabling the model to effectively learn linguistic patterns across different languages and sources. Prior to training, the textual data is carefully pre-processed through cleaning, normalization, and tokenization to improve classification accuracy and reduce unnecessary computational overhead. The DistilBERT model is then fine-tuned to classify news content as real or fake based on semantic and contextual information. Extensive experiments conducted on social media–based text data demonstrate that the proposed approach achieves an accuracy of 88.97% and a precision of 0.99 in detecting fake misinformation. These results highlight the effectiveness of lightweight Transformer-based architectures in identifying misinformation while maintaining efficiency and scalability. The study confirms that artificial intelligence–driven and deep learning–based models can play a significant role in mitigating the spread of fake news and improving the reliability of online information ecosystems.

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.