Fake News Detection Research Articles

Fake News Detection

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

Enhancement of random forest algorithm applied in fake news detection

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

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

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

Survey of machine learning techniques for Arabic fake news detection

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

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

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

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

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

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

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

Advancements in Fake News Detection: A Comprehensive Machine Learning Approach Across Varied Datasets

Advancements in Fake News Detection: A Comprehensive Machine Learning Approach Across Varied Datasets

Machine learning base algorithms using truncated singular value decomposition as a novel solution for fake news analysis and detection

Machine learning base algorithms using truncated singular value decomposition as a novel solution for fake news analysis and detection

Multimodal fake news detection through intra-modality feature aggregation and inter-modality semantic fusion

The prevalence of online misinformation, termed “fake news”, has exponentially escalated in recent years. These deceptive information, often rich with multimodal content, can easily deceive individuals into spreading them via various social media platforms. This has made it a hot research topic to automatically detect multimodal fake news. Existing works made a great progress on inter-modality feature fusion or semantic interaction yet largely ignore the importance of intra-modality entities and feature aggregation. This imbalance causes them to perform erratically on data with different emphases. In the realm of authentic news, the intra-modality contents and the inter-modality relationship should be in mutually supportive relationships. Inspired by this idea, we propose an innovative approach to multimodal fake news detection (IFIS), incorporating both intra-modality feature aggregation and inter-modality semantic fusion. Specifically, the proposed model implements a entity detection module and utilizes attention mechanisms for intra-modality feature aggregation, whereas inter-modality semantic fusion is accomplished via two concurrent Co-attention blocks. The performance of IFIS is extensively tested on two datasets, namely Weibo and Twitter, and has demonstrated superior performance, surpassing various advanced methods by 0.6 The experimental results validate the capability of our proposed approach in offering the most balanced performance for multimodal fake news detection tasks.

Automated and Interpretable Fake News Detection With Explainable Artificial Intelligence

Fake news is a piece of misleading or forged information that affects society, business, governments, etc., hence is an imperative issue. The solution presented here to detect fake news involves purely using rigorous machine learning approaches in implementing a hybrid of simple yet accurate fake text detection models and fake image detection models to detect fake news. The solution considers the text and images of any news article, extracted using web scraping, where the text segment of a news article is analyzed using an ensemble model of the Naïve Bayes, Random Forest, and Decision Tree classifier, which showed improved results than the individual models. The image segment of a news article is analyzed using only a Convolution Neural Network, which showed optimal accuracy similar to the text model. To better train the text models, data preprocessing and aggregation methods were used to combine various fake-real news datasets to have ample amounts of data. Similarly, the CASIA dataset was used to train the image model, over which Error Level Analysis was performed to detect fake images. model results are represented as confusion matrices and are measured using various performance metrics. Also, to explain predictions from the hybrid model, Explainable Artificial Intelligence is used.

GraMuFeN: graph-based multi-modal fake news detection in social media

GraMuFeN: graph-based multi-modal fake news detection in social media

Emotions unveiled: detecting COVID-19 fake news on social media

The COVID-19 pandemic has highlighted the pernicious effects of fake news, underscoring the critical need for researchers and practitioners to detect and mitigate its spread. In this paper, we examined the importance of detecting fake news and incorporated sentiment and emotional features to detect this type of news. Specifically, we compared the sentiments and emotions associated with fake and real news using a COVID-19 Twitter dataset with labeled categories. By utilizing different sentiment and emotion lexicons, we extracted sentiments categorized as positive, negative, and neutral and eight basic emotions, anticipation, anger, joy, sadness, surprise, fear, trust, and disgust. Our analysis revealed that fake news tends to elicit more negative emotions than real news. Therefore, we propose that negative emotions could serve as vital features in developing fake news detection models. To test this hypothesis, we compared the performance metrics of three machine learning models: random forest, support vector machine (SVM), and Naïve Bayes. We evaluated the models’ effectiveness with and without emotional features. Our results demonstrated that integrating emotional features into these models substantially improved the detection performance, resulting in a more robust and reliable ability to detect fake news on social media. In this paper, we propose the use of novel features and methods that enhance the field of fake news detection. Our findings underscore the crucial role of emotions in detecting fake news and provide valuable insights into how machine-learning models can be trained to recognize these features.

Fake News Detection System Using Logistic Regression, Decision Tree and Random Forest

The purpose of this study is to design a fake news detection system with these three machine learning models, namely: Decision Tree, Random Forest, and Logistic Regression. These three different models were analysed to determine the most efficient model for accurately detecting fake news. The result obtained showcased Logistic Regression with an accuracy of 98.80%, Decision Tree with an accuracy of 99.64% and Random Forest with an accuracy of 99.23%. It is evident as deduction from the comparative analysis that our best model came out to be Decision Tree with an accuracy of 99.64%.

Fake News Detection using Machine Learning

Fake News Detection using Machine Learning

Enhanced Multi-Class Detection of Fake News

Enhanced Multi-Class Detection of Fake News

Web information extraction and fake news detection in twitter using optimized hybrid bi-gated deep learning network

Web information extraction and fake news detection in twitter using optimized hybrid bi-gated deep learning network

Fake News Detector

The proliferation of fake news in digital media presents a significant challenge to information integrity. This research explores the application of machine learning, specifically logistic regression, for automated fake news detection using a dataset sourced from Kaggle. Text preprocessing techniques, including tokenization, stemming, and TF-IDF vectorization, were applied to extract features from news articles. A logistic regression model was trained on the processed data to classify articles as real or fake. The model achieved high accuracy rates of 98.68% on the training set and 97.67% on the testing set. Additionally, a user-friendly Streamlit web application was developed for real-time prediction of fake news. This study demonstrates the efficacy of logistic regression in combatting misinformation and contributes to enhancing information credibility in digital media. Top of Form Key Words — Fake news detection, machine learning, logistic regression, TF-IDF vectorization, text preprocessing, information integrity.

An Adaptive Word Vector Integrated With Enhanced Lstm For Classification Of Fake News

Social-media has great impact on society by spreading news which can be fake or real without analysis. Before confessing the news predictions has to be considered. To analyse a text through machines only NLP was best method. This focuses on five stages of NLP for detection of fake or real news. For creating vector to words a neural network technique word2vec was utilized for accurate prediction of word. For identifying meaning full words tokenization was utilized. By creating the vectors, the words have to be embedded for inputting the data to train and feature learning. In dense and dropout, the neural network method LSTM is initialized for the extraction of features or words. Dropout layer involves LSTM method for co-adopt the internal data. These layers mitigate overfitting by stochastically disabling neurons during training, hence fostering resilient feature acquisition. Dense layers, by use of non-linear mappings, allow the network to comprehend intricate patterns in data. This extensive investigation offers valuable insights into the development and fine-tuning of NLP models, which in turn enhances the accuracy of false news detection. To evaluate the performances epochs are utilized were accuracy and loss was validated. Five epochs are calculated were accuracy was gradually increased and loss had been decreased. The highest accuracy achieved was 99.8% and low loss value 0.01.

Fake news detection in low-resource languages: A novel hybrid summarization approach

The proliferation of fake news across languages and domains on social media platforms poses a significant societal threat. Current automatic detection methods for low-resource languages (e.g., Swahili, Indonesian and other low-resource languages) face limitations due to two factors: sequential length restrictions in pre-trained language models (PLMs) like multilingual bidirectional encoder representation from transformers (mBERT), and the presence of noisy training data. This work proposes a novel and efficient multilingual fake news detection (MFND) approach that addresses these challenges. Our solution leverages a hybrid extractive and abstractive summarization strategy to extract only the most relevant content from news articles. This significantly reduces data length while preserving crucial information for fake news classification. The pre-processed data is then fed into mBERT for classification. Extensive evaluations on a publicly available multilingual dataset demonstrate the superiority of our approach compared to state-of-the-art (SOTA) methods. Our analysis, both quantitative and qualitative, highlights the strengths of this method, achieving new performance benchmarks and emphasizing the impact of content condensation on model accuracy and efficiency. This framework paves the way for faster, more accurate MFND, fostering more robust information ecosystems.