Fake News Classification Research Articles

Detecting Fake News Using Data Analytics: A Systematic Literature Review And Machine Learning Approach

This study systematically reviews advancements in fake news detection techniques, examining methodologies and frameworks across machine learning, natural language processing, social network analysis, and multimodal approaches. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a rigorous and transparent review process was conducted, resulting in the selection and evaluation of 254 research articles. The findings reveal that supervised learning models, such as Support Vector Machines, Decision Trees, and Naïve Bayes, have shown strong performance in text-based fake news classification, particularly when feature selection is optimized. Deep learning models, including CNNs, RNNs, and transformers, have further advanced detection accuracy by capturing complex linguistic patterns, though challenges with computational demands and model interpretability remain. In scenarios with limited labeled data, unsupervised learning models and semi-supervised approaches offer adaptability, with clustering, anomaly detection, and iterative self-labeling proving effective for evolving misinformation. Additionally, cross-disciplinary approaches, integrating insights from psychology, sociology, and network science, enhance detection models by accounting for user behavior, emotional appeal, and social conformity in the spread of fake news. Case studies from collaborative projects underscore the potential of interdisciplinary efforts to develop robust, adaptable detection frameworks. This review concludes that effective fake news detection requires a multifaceted approach, combining technical advancements with social science insights to address the complexity and adaptive nature of misinformation. The study emphasizes the need for continued research in hybrid models and adaptive, real-time detection solutions to strengthen defenses against fake news in diverse digital environments.

Text-image multimodal fusion model for enhanced fake news detection.

In the era of rapid internet expansion and technological progress, discerning real from fake news poses a growing challenge, exposing users to potential misinformation. The existing literature primarily focuses on analyzing individual features in fake news, overlooking multimodal feature fusion recognition. Compared to single-modal approaches, multimodal fusion allows for a more comprehensive and enriched capture of information from different data modalities (such as text and images), thereby improving the performance and effectiveness of the model. This study proposes a model using multimodal fusion to identify fake news, aiming to curb misinformation. The framework integrates textual and visual information, using early fusion, joint fusion and late fusion strategies to combine them. The proposed framework processes textual and visual information through data cleaning and feature extraction before classification. Fake news classification is accomplished through a model, achieving accuracy of 85% and 90% in the Gossipcop and Fakeddit datasets, with F1-scores of 90% and 88%, showcasing its performance. The study presents outcomes across different training periods, demonstrating the effectiveness of multimodal fusion in combining text and image recognition for combating fake news. This research contributes significantly to addressing the critical issue of misinformation, emphasizing a comprehensive approach for detection accuracy enhancement.

Indonesian Fake News Classification Using Transfer Learning in CNN and LSTM

Fake news spreads quickly and is challenging to stop due to the ease of accessing and sharing information online. Deep learning techniques are a method that can be used to identify fake news quickly and accurately. The types of neural networks commonly utilized in deep learning architectures include Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), which can perform well when managing the task of classifying fake news, according to several pertinent studies. Regarding handling instances of Indonesian fake news classification, this study compares how well the CNN and LSTM models perform. However, given that Indonesian is a low-resource language with scant documentation, it is challenging to build an adequate data set. At the same time, the CNN and LSTM classification models require significant training data. We proposed a transfer learning method by combining two classification models with a pre-trained IndoBERT language model. 1340 news text data were used, including 643 actual news texts from CNN Indonesia, Liputan6, and Detik and 697 fake news texts from TurnBackHoax. As a result, the performance of the combination of the LSTM classification model with IndoBERT outperformed that of the CNN classification model with IndoBERT, which only produced an accuracy of 92.91%, down by 6%, and was able to produce an accuracy of up to 97.76%, an increase of 4.8% from before. Furthermore, the results show that the LSTM classification model outperforms the CNN classification model in capturing the representation created by IndoBERT. Additionally, these insights may serve as a basis for future research on identifying fake news in Indonesia, helping to improve methods for combatting misinformation in Indonesia.

FAKE NEWS DETECTION

The Internet is one of among the greatest inventions of the world and there are millions of individuals who utilize it. These persons employ it in various ways, as listed below: There are various social networks that are available for use among such users. Often they can be just ordinaries who decide to make a post or share the news on the internet. Such platforms offer no means of confirming any users or the content they post. Therefore, some of the users attempt to indulge in active dissemination of fake news through the platforms. This fake news can aimed at an individual, a community, a company or a political party. It becomes virtually impossible for a human being to follow all the fake news. So, currently, there exists the need for automatic classification of fake news using machine learning classifiers. My description of machine learning classifiers such as passive agressive classifiers and algorithm such as K-Nearest Neighbor, Support vector machine(SVM) is used for detecting fake news is described in this systematic literature review. KEYWORD: Fake News Dissemination, Social Networks, Automatic Classification, Machine Learning Classifiers, Passive Aggressive Classifiers, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Systematic Literature Review, Challenges in Fake News Detection.

A Predictive Model for Benchmarking the Performance of Algorithms for Fake and Counterfeit News Classification in Global Networks.

The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected algorithms. Algorithms such as Passive Aggressive Classifier, perceptron, and decision stump undergo meticulous refinement for text classification tasks, leveraging 29 models trained on diverse social media datasets. Sensors can be utilized for data collection. Data preprocessing involves rigorous cleansing and feature vector generation using TF-IDF and Count Vectorizers. The models' efficacy in classifying genuine news from falsified or exaggerated content is evaluated using metrics like accuracy, precision, recall, and more. In order to obtain the best-performing algorithm from each of the datasets, a predictive model was developed, through which SG with 0.681190 performs best in Dataset 1, BernoulliRBM has 0.933789 in Dataset 2, LinearSVC has 0.689180 in Dataset 3, and BernoulliRBM has 0.026346 in Dataset 4. This research illuminates strategies for classifying fake news, offering potential solutions to ensure information integrity and democratic discourse, thus carrying profound implications for academia and real-world applications. This work also suggests the strength of sensors for data collection in IoT environments, big data analytics for smart cities, and sensor applications which contribute to maintaining the integrity of information within urban environments.

Analysis and Classification of Fake News Using Sequential Pattern Mining

Analysis and Classification of Fake News Using Sequential Pattern Mining

GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection

The digital era has expanded social exposure with easy internet access for mobile users, allowing for global communication. Now, people can get to know what is going on around the globe with just a click; however, this has also resulted in the issue of fake news. Fake news is content that pretends to be true but is actually false and is disseminated to defraud. Fake news poses a threat to harmony, politics, the economy, and public opinion. As a result, bogus news detection has become an emerging research domain to identify a given piece of text as genuine or fraudulent. In this paper, a new framework called Generative Bidirectional Encoder Representations from Transformers (GBERT) is proposed that leverages a combination of Generative pre-trained transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) and addresses the fake news classification problem. This framework combines the best features of both cutting-edge techniques—BERT's deep contextual understanding and the generative capabilities of GPT—to create a comprehensive representation of a given text. Both GPT and BERT are fine-tuned on two real-world benchmark corpora and have attained 95.30 % accuracy, 95.13 % precision, 97.35 % sensitivity, and a 96.23 % F1 score. The statistical test results indicate the effectiveness of the fine-tuned framework for fake news detection and suggest that it can be a promising approach for eradicating this global issue of fake news in the digital landscape.

Development of an enhanced naive bayes algorithm for fake news classification

Development of an enhanced naive bayes algorithm for fake news classification

Application of Machine Learning Techniques for Fake News Classification

ABSTRACT Fake News consists of disseminating fake news in various social and digital media such as newspapers, television networks, and the internet. Fake news is not a new phenomenon in human behavior. However, the current dissemination is very different from what happened in the past. Social networks and the contemporary world have made it possible for the spread of lies to occur quickly and even intentionally. This causes serious problems, and its impacts can be felt in the real world. The identification of fake news can be useful in several contexts and can be used, for example, as a news filter in the virtual space. Thus, the present work aims to propose and evaluate strategies for processing and applying machine learning models, to improve the performance of classifiers in the problem of identifying fake news in Brazilian news.

Quantum-inspired firefly algorithm with ant miner plus for fake news detection

Nowadays, technology has shifted the way individuals access news from conventional media sources to social media platforms. The active engagement of people with social media platforms leads them to consume news without confirming its source or legitimacy. This facilitated the dissemination of more manipulated and false information in the form of rumors and fake news. Fake news can affect public opinion and create chaos and panic among the population. Thus, it is essential to employ an advanced methodology to identify fake news with high precision. This research work has proposed the concept of the quantum-inspired firefly algorithm with the ant miner plus algorithm (QFAMP) for more effective fake news detection. The proposed QFAMP algorithm utilizes the attributes of quantum computing (QC), the firefly algorithm (FA), and the ant miner plus algorithm (AMP). Here, the QFA approach ensures the effective exploitation of the firefly agents until the agents are able to search for the brighter firefly. Further, the AMP algorithm utilizes the best ants with higher pheromone concentrations for global exploration, which also avoids the premature convergence of the QFA agents. In addition, the AMP algorithm serves as an efficient data mining variant that is effective for the classification of fake news. The efficacy of the proposed QFAMP algorithm is evaluated for the dataset of FakeNewsNet, which is composed of two sub-categories: BuzzFeed and PolitiFact. The experimental evaluations indicate the effective performance of the proposed algorithm compared to the other techniques.

Fake News Detection and Classification with Recurrent Neural Network Based Deep Learning Approaches

Events happening in the world are transmitted to the end user through the news channel. The information transmitted from the news is generally considered to be accurate. However, there may be errors or lies in the information that circulates on the news channels. At the same time, this news has an impact on serious environments, such as the economy. In social networks where data sharing is increasing, news data is piling up uncontrollably. In these data piles, there is real information as well as different information that is not real commercial, political, or sales-orientated. False information and data expand faster as a result of sharing false information by users. This news directly affects users, causing erroneous transactions, misinformation, or financial loss. For the stated reasons, automatic fake news classification systems are proposed in this article by combining natural language processing with Recurrent Neural Network (RNN) based deep learning methods. The proposed systems were tested on a dataset containing 23,481 fake news and 21,417 real news using general performance metrics. As a result of the test processes, the proposed BiLSTM method provided 99,72% accuracy, while the proposed GRU method accessed 97,50% accuracy.

Word embedding and classification methods and their effects on fake news detection

Natural language processing contains multiple methods of translating written text or spoken words into numerical information called word embeddings. Some of these embedding methods, such as Bag of Words, assume words are independent of one another. Other embedding methods, such as Bidirectional Encoder Representations from Transformers and Word2Vec, capture the relationship between words in various ways. In this paper, we are interested in comparing methods treating words as independent and methods capturing the relationship between words by looking at the effect these methods have on the classification of fake news. Using various classification methods, we compare the word embedding processes based on their effects on accuracy, precision, sensitivity, and specificity.

Evaluating text classification: A benchmark study

This paper presents an impartial and extensive benchmark for text classification involving five different text classification tasks, 20 datasets, 11 different model architectures, and 42,800 algorithm runs. The five text classification tasks are fake news classification, topic detection, emotion detection, polarity detection, and sarcasm detection. While in practice, especially in Natural Language Processing (NLP), research tends to focus on the most sophisticated models, we hypothesize that this is not always necessary. Therefore, our main objective is to investigate whether the largest state-of-the-art (SOTA) models are always preferred, or in what cases simple methods can compete with complex models, i.e. for which dataset specifications and classification tasks. We assess the performance of different methods with varying complexity, ranging from simple statistical and machine learning methods to pretrained transformers like robustly optimized BERT (Bidirectional Encoder Representations from Transformers) pretraining approach (RoBERTa). This comprehensive benchmark is lacking in existing literature, with research mainly comparing similar types of methods. Furthermore, with increasing awareness of the ecological impacts of extensive computational resource usage, this comparison is both critical and timely.We find that overall, bidirectional long short-term memory (LSTM) networks are ranked as the best-performing method albeit not statistically significantly better than logistic regression and RoBERTa. Overall, we cannot conclude that simple methods perform worse although this depends mainly on the classification task. Concretely, we find that for fake news classification and topic detection, simple techniques are the best-ranked models and consequently, it is not necessary to train complicated neural network architectures for these classification tasks. Moreover, we also find a negative correlation between F1 performance and complexity for the smallest datasets (with dataset size less than 10,000). Finally, the different models’ results are analyzed in depth to explain the model decisions, which is an increasing requirement in the field of text classification.

Towards Optimal NLP Solutions: Analyzing GPT and LLaMA-2 Models Across Model Scale, Dataset Size, and Task Diversity

This study carries out a comprehensive comparison of fine-tuned GPT models (GPT-2, GPT-3, GPT-3.5) and LLaMA-2 models (LLaMA-2 7B, LLaMA-2 13B, LLaMA-2 70B) in text classification, addressing dataset sizes, model scales, and task diversity. Since its inception in 2018, the GPT series has been pivotal in advancing NLP, with each iteration introducing substantial enhancements. Despite its progress, detailed analyses, especially against competitive open-source models like the LLaMA-2 series in text classification, remain scarce. The current study fills this gap by fine-tuning these models across varied datasets, focusing on enhancing task-specific performance in hate speech and offensive language detection, fake news classification, and sentiment analysis. The learning efficacy and efficiency of the GPT and LLaMA-2 models were evaluated, providing a nuanced guide to choosing optimal models for NLP tasks based on architectural benefits and adaptation efficiency with limited data and resources. In particular, even with datasets as small as 1,000 rows per class, the F1 scores for the GPT-3.5 and LLaMA-2 models exceeded 0.9, reaching 0.99 with complete datasets. Additionally, the LLaMA-2 13B and 70B models outperformed GPT-3, demonstrating their superior efficiency and effectiveness in text classification. Both the GPT and LLaMA-2 series showed commendable performance on all three tasks, underscoring their ability to handle a diversity of tasks. Based on the size, performance, and resources required for fine-tuning the model, this study identifies LLaMA-2 13B as the most optimal model for NLP tasks.

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.

ANN: adversarial news net for robust fake news classification

With easy access to social media platforms, spreading fake news has become a growing concern today. Classifying fake news is essential, as it can help prevent its negative impact on individuals and society. In this regard, an end-to-end framework for fake news detection is developed by utilizing the power of adversarial training to make the model more robust and resilient. The framework is named "ANN: Adversarial News Net," emoticons have been extracted from the datasets to understand their meanings concerning fake news. This information is then fed into the model, which helps to improve its performance in classifying fake news. The performance of the ANN framework is evaluated using four publicly available datasets, and it is found to outperform baseline methods and previous studies after adversarial training. Experiments show that Adversarial Training improved the performance by 2.1% over the Random Forest baseline and 2.4% over the BERT baseline method in terms of accuracy. The proposed framework can be used to detect fake news in real-time, thereby mitigating its harmful effects on society.

Fake News Classification Using Tensor Decomposition and Graph Convolutional Network

With the widespread of fake news on social media, its impact has become a major concern of the public, so accurate detection methods are urgently needed. As content-based features are the most natural clues for fake news detection, many methods that rely solely on text content have been proposed. However, these methods rarely investigate the sentence interaction patterns of different news articles, and most of them do not consider fine-grained fake news classification. To overcome these issues, this article constructs a graph representation for news articles and employs a graph neural network (GNN) to classify fake news. The proposed method uses the local word co-occurrence information of sentences to obtain the interaction relationship between sentences, which is abstracted by the weight matrix of the graph representation. Accordingly, a third-order co-occurrence tensor is built, and the weight matrix is calculated based on the canonical polyadic (CP) decomposition of this tensor. Since our method considers the sentence interaction patterns, the computed representations can capture more accurate contextual information of news articles. The results on two real-world datasets demonstrate that our method outperforms the competing methods in both binary and multiclass classification tasks. In particular, for multiclass classification on the selected dataset with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$70$</tex-math> </inline-formula> % of the training set for training, the improvements of accuracy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$</tex-math> </inline-formula> -score are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.82$</tex-math> </inline-formula> %p– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16.98$</tex-math> </inline-formula> %p and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.85$</tex-math> </inline-formula> %p– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16.65$</tex-math> </inline-formula> %p, respectively.

Fake News Prediction and Analysis in LIAR Dataset Using Advanced Machine Learning Techniques

Fake news detection and prediction is the crucial research issue in now a day because it is very difficult to know the news authenticity on social media. It has a devastating impact on societies and democratic institutions as online life in these days are one of the principal news hotspots for many individuals around the world because of their minimal effort, simple access, and quickly spread of the unauthorized news. However, measurable ways to deal with battling fake news have been drastically restricted by the absence of named benchmark datasets. Smart machine learning classifiers are used to solve the problem of fake news prediction and classification. The proposed research study works on the LIAR dataset, the open-source available dataset for fake news classification with 12.8K decade-long, hand-labelled short statements in various contexts. The proposed research study has used a novel approach to deal with the fake news prediction accurately and this approach outperforms in this scenario for the same dataset. Naïve Bayes classifier for classification is used to reduce the variance values in the dataset to get rid of the overfitting issue. This classifier shows more improved results than other prior classifiers and the accuracy value was 99%. The proposed research study performed experiments and evaluated through different evaluation measures, the results of accuracy for the Naïve Bayes are best as compared to Random Forest, Decision tree, and Neural Networks are computed for each algorithm. The proposed research study could be applied in real-time applications to deal the fake news prediction in social media and digital media platforms.

An efficient fake news classification model based on ensemble deep learning techniques

The availability and expansion of social media has made it difficult to distinguish between fake and real news. Information falsification has exponentially increased as a result of how simple it is to spread information through sharing. Social media dependability is also under jeopardy due to the extensive dissemination of false information. Therefore, it has become a research problem to automatically validate information, specifically source, content, and publisher, to identify it as true or false. Despite its limitations, machine learning (ML) has been crucial in the categorization of information. Previous studies suggested three-step methods for categorising false information on social media. In the first step of the process, the data set is subjected to a number of pre-processing processes in order to transform unstructured data sets into structured data sets. The unknowable properties of fake news and the features are extracted by the Lexicon Model in the second stage. In the third stage of this research project, a feature selection method by WOA (Whale Optimization Algorithm) for weight value to tune the classification part. Finally, a Hybrid Classification model that is hybrid with a fuzzy based Convolutional Neural Network and kernel based support vector machine is constructed in order to identify the data pertaining to bogus news. However using single classifier for fake news detection produces the insufficient accuracy. To overcome this issue in this work introduced an improved model for fake news classification. To turn unstructured data sets into structured data sets, a variety of pre-processing operations are used on the data set in the initial phase of the procedure. The unknowable properties of fake news and the features are extracted by the Lexicon Model in the second stage. In the third stage of this research project, a feature selection method by COA (Coati Optimization Algorithm) for weight value to tune the classification part. Finally, an ensemble of RNN (Recurrent Neural Networks), VGG-16 and ResNet50.A classification model was developed to recognise bogus news information. Evaluate each fake news analysis' performance in terms of accuracy, precision, recall, and F1 score. The suggested model, out of all the methodologies taken into consideration in this study, provides the highest outcomes, according to experimental findings