Social media has significantly accelerated the rapid dissemination of information,but it also boosts propagation of fake news,posing serious challenges to public awareness and social stability.In real-world contexts,t...Social media has significantly accelerated the rapid dissemination of information,but it also boosts propagation of fake news,posing serious challenges to public awareness and social stability.In real-world contexts,the volume of trustable information far exceeds that of rumors,resulting in a class imbalance that leads models to prioritize the majority class during training.This focus diminishes the model’s ability to recognize minority class samples.Furthermore,models may experience overfitting when encountering these minority samples,further compromising their generalization capabilities.Unlike node-level classification tasks,fake news detection in social networks operates on graph-level samples,where traditional interpolation and oversampling methods struggle to effectively generate high-quality graph-level samples.This challenge complicates the identification of new instances of false information.To address this issue,this paper introduces the FHGraph(Fake News Hunting Graph)framework,which employs a generative data augmentation approach and a latent diffusion model to create graph structures that align with news communication patterns.Using the few-sample learning capabilities of large language models(LLMs),the framework generates diverse texts for minority class nodes.FHGraph comprises a hierarchical multiview graph contrastive learning module,in which two horizontal views and three vertical levels are utilized for self-supervised learning,resulting in more optimized representations.Experimental results show that FHGraph significantly outperforms state-of-the-art(SOTA)graph-level class imbalance methods and SOTA graph-level contrastive learning methods.Specifically,FHGraph has achieved a 2%increase in F1 Micro and a 2.5%increase in F1 Macro in the PHEME dataset,as well as a 3.5%improvement in F1 Micro and a 4.3%improvement in F1 Macro on RumorEval dataset.展开更多
These days,social media has grown to be an integral part of people’s lives.However,it involves the possibility of exposure to“fake news”,which may contain information that is intentionally or inaccurately false to ...These days,social media has grown to be an integral part of people’s lives.However,it involves the possibility of exposure to“fake news”,which may contain information that is intentionally or inaccurately false to promote particular political or economic interests.The main objective of this work is to use the co-attention mechanism in a Combined Graph neural network model(CMCG)to capture the relationship between user profile features and user preferences in order to detect fake news and examine the influence of various social media features on fake news detection.The proposed approach includes three modules.The first one creates a Graph Neural Network(GNN)based model to learn user profile properties,while the second module encodes news content,user historical posts,and news sharing cascading on social media as user preferences GNN-based model.The inter-dependencies between user profiles and user preferences are handled through the third module using a co-attention mechanism for capturing the relationship between the two GNN-based models.We conducted several experiments on two commonly used fake news datasets,Politifact and Gossipcop,where our approach achieved 98.53%accuracy on the Gossipcop dataset and 96.77%accuracy on the Politifact dataset.These results illustrate the effectiveness of the CMCG approach for fake news detection,as it combines various information from different modalities to achieve relatively high performances.展开更多
With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of...With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of multimodal approaches for fake news detection has gained significant attention.To solve the problems existing in previous multi-modal fake news detection algorithms,such as insufficient feature extraction and insufficient use of semantic relations between modes,this paper proposes the MFFFND-Co(Multimodal Feature Fusion Fake News Detection with Co-Attention Block)model.First,the model deeply explores the textual content,image content,and frequency domain features.Then,it employs a Co-Attention mechanism for cross-modal fusion.Additionally,a semantic consistency detectionmodule is designed to quantify semantic deviations,thereby enhancing the performance of fake news detection.Experimentally verified on two commonly used datasets,Twitter and Weibo,the model achieved F1 scores of 90.0% and 94.0%,respectively,significantly outperforming the pre-modified MFFFND(Multimodal Feature Fusion Fake News Detection with Attention Block)model and surpassing other baseline models.This improves the accuracy of detecting fake information in artificial intelligence detection and engineering software detection.展开更多
Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking an...Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.展开更多
As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocrea...As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensively represent themultifaceted informationof fake news.展开更多
In recent years,social media platforms have gained immense popularity.As a result,there has been a tremendous increase in content on social media platforms.This content can be related to an individual’s sentiments,th...In recent years,social media platforms have gained immense popularity.As a result,there has been a tremendous increase in content on social media platforms.This content can be related to an individual’s sentiments,thoughts,stories,advertisements,and news,among many other content types.With the recent increase in online content,the importance of identifying fake and real news has increased.Although,there is a lot of work present to detect fake news,a study on Fuzzy CRNN was not explored into this direction.In this work,a system is designed to classify fake and real news using fuzzy logic.The initial feature extraction process is done using a convolutional recurrent neural network(CRNN).After the extraction of features,word indexing is done with high dimensionality.Then,based on the indexing measures,the ranking process identifies whether news is fake or real.The fuzzy CRNN model is trained to yield outstanding resultswith 99.99±0.01%accuracy.This work utilizes three different datasets(LIAR,LIAR-PLUS,and ISOT)to find the most accurate model.展开更多
In view of the various adverse effects,fake news detection has become an extremely important task.So far,many detection methods have been proposed,but these methods still have some limitations.For example,only two ind...In view of the various adverse effects,fake news detection has become an extremely important task.So far,many detection methods have been proposed,but these methods still have some limitations.For example,only two independently encoded unimodal information are concatenated together,but not integrated with multimodal information to complete the complementary information,and to obtain the correlated information in the news content.This simple fusion approach may lead to the omission of some information and bring some interference to the model.To solve the above problems,this paper proposes the FakeNewsDetectionmodel based on BLIP(FNDB).First,the XLNet and VGG-19 based feature extractors are used to extract textual and visual feature representation respectively,and BLIP based multimodal feature extractor to obtain multimodal feature representation in news content.Then,the feature fusion layer will fuse these features with the help of the cross-modal attention module to promote various modal feature representations for information complementation.The fake news detector uses these fused features to identify the input content,and finally complete fake news detection.Based on this design,FNDB can extract as much information as possible from the news content and fuse the information between multiple modalities effectively.The fake news detector in the FNDB can also learn more information to achieve better performance.The verification experiments on Weibo and Gossipcop,two widely used real-world datasets,show that FNDB is 4.4%and 0.6%higher in accuracy than the state-of-theart fake news detection methods,respectively.展开更多
The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social media.Indi...The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social media.Individuals can quickly fabricate comments and news on social media.The most difficult challenge is determining which news is real or fake.Accordingly,tracking down programmed techniques to recognize fake news online is imperative.With an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media content.This study shows past,current,and possible methods that can be used in the future for fake news classification.Two different publicly available datasets containing political news are utilized for performing experiments.Sixteen supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text classification.In contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer text.Additionally,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.展开更多
In the past few years,social media and online news platforms have played an essential role in distributing news content rapidly.Consequently.verification of the authenticity of news has become a major challenge.During...In the past few years,social media and online news platforms have played an essential role in distributing news content rapidly.Consequently.verification of the authenticity of news has become a major challenge.During the COVID-19 outbreak,misinformation and fake news were major sources of confusion and insecurity among the general public.In the first quarter of the year 2020,around 800 people died due to fake news relevant to COVID-19.The major goal of this research was to discover the best learning model for achieving high accuracy and performance.A novel case study of the Fake News Classification using ELECTRA model,which achieved 85.11%accuracy score,is thus reported in this manuscript.In addition to that,a new novel dataset called COVAX-Reality containing COVID-19 vaccine-related news has been contributed.Using the COVAX-Reality dataset,the performance of FNEC is compared to several traditional learning models i.e.,Support Vector Machine(SVM),Naive Bayes(NB),Passive Aggressive Classifier(PAC),Long Short-Term Memory(LSTM),Bi-directional LSTM(Bi-LSTM)and Bi-directional Encoder Representations from Transformers(BERT).For the evaluation of FNEC,standard metrics(Precision,Recall,Accuracy,and F1-Score)were utilized.展开更多
This paper proposes an original behavioural finance representative agent model,to explain how fake news’empirical price impacts can persist in finance despite contradicting the efficient-market hypothesis.The model r...This paper proposes an original behavioural finance representative agent model,to explain how fake news’empirical price impacts can persist in finance despite contradicting the efficient-market hypothesis.The model reconciles empirically-observed price overreactions to fake news with empirically-observed price underreactions to real news,and predicts a novel secondary impact of fake news:that fake news in a security amplifies underreactions to subsequent real news for the security.Evaluating the model against a large-sample event study of the 2019 Chinese ADR Delisting Threat fake news and debunking event,this paper finds strong qualitative validation for its model’s dynamics and predictions.展开更多
Following the development of communication techniques and smart devices,the era of Artificial Intelligence(AI)and big data has arrived.The increased connectivity,referred to as hyper-connectivity,has led to the develo...Following the development of communication techniques and smart devices,the era of Artificial Intelligence(AI)and big data has arrived.The increased connectivity,referred to as hyper-connectivity,has led to the development of smart cities.People in these smart cities can access numerous online contents and are always connected.These developments,however,also lead to a lack of standardization and consistency in the propagation of information throughout communities due to the consumption of information through social media channels.Information cannot often be verified,which can confuse the users.The increasing influence of social media has thus led to the emergence and increasing prevalence of fake news.In this study,we propose a methodology to classify and identify fake news emanating from social channels.We collected content from Twitter to detect fake news and statistically verified that the temporal propagation pattern of quote retweets is effective for the classification of fake news.To verify this,we trained the temporal propagation pattern to a two-phases deep learning model based on convolutional neural networks and long short-term memory.The fake news classifier demonstrates the ability for its early detection.Moreover,it was verified that the temporal propagation pattern was the most influential feature compared to other feature groups discussed in this paper.展开更多
Social media has become increasingly significant in modern society,but it has also turned into a breeding ground for the propagation of misleading information,potentially causing a detrimental impact on public opinion...Social media has become increasingly significant in modern society,but it has also turned into a breeding ground for the propagation of misleading information,potentially causing a detrimental impact on public opinion and daily life.Compared to pure text content,multmodal content significantly increases the visibility and share ability of posts.This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection.To effectively address the critical challenge of accurately detecting fake news on social media,this paper proposes a fake news detection model based on crossmodal message aggregation and a gated fusion network(MAGF).MAGF first uses BERT to extract cumulative textual feature representations and word-level features,applies Faster Region-based ConvolutionalNeuralNetwork(Faster R-CNN)to obtain image objects,and leverages ResNet-50 and Visual Geometry Group-19(VGG-19)to obtain image region features and global features.The image region features and word-level text features are then projected into a low-dimensional space to calculate a text-image affinity matrix for cross-modal message aggregation.The gated fusion network combines text and image region features to obtain adaptively aggregated features.The interaction matrix is derived through an attention mechanism and further integrated with global image features using a co-attention mechanism to producemultimodal representations.Finally,these fused features are fed into a classifier for news categorization.Experiments were conducted on two public datasets,Twitter and Weibo.Results show that the proposed model achieves accuracy rates of 91.8%and 88.7%on the two datasets,respectively,significantly outperforming traditional unimodal and existing multimodal models.展开更多
In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure in...In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical.Unfortunately,existing approaches fail to handle these problems.This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues(TD-MMC),which utilizes three valuable multi-model clues:text-model importance,text-image complementary,and text-image inconsistency.TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation.To reduce the irrelevant social structure’s information interference,we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features.A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information.In addition,TD-MMC employs a new multi-model loss to improve the model’s generalization ability.Extensive experiments have been conducted on two public real-world English and Chinese datasets,and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.展开更多
In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of...In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of distinguishing between truthful and deceptive information.Fake news,a prevalent issue,particularly on social media,complicates the assessment of news credibility.The pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources,creating confusion and polarizing opinions.As the volume of information grows,individuals increasingly struggle to discern credible content from false narratives,leading to widespread misinformation and potentially harmful consequences.Despite numerous methodologies proposed for fake news detection,including knowledge-based,language-based,and machine-learning approaches,their efficacy often diminishes when confronted with high-dimensional datasets and data riddled with noise or inconsistencies.Our study addresses this challenge by evaluating the synergistic benefits of combining feature extraction and feature selection techniques in fake news detection.We employ multiple feature extraction methods,including Count Vectorizer,Bag of Words,Global Vectors for Word Representation(GloVe),Word to Vector(Word2Vec),and Term Frequency-Inverse Document Frequency(TF-IDF),alongside feature selection techniques such as Information Gain,Chi-Square,Principal Component Analysis(PCA),and Document Frequency.This comprehensive approach enhances the model’s ability to identify and analyze relevant features,leading to more accurate and effective fake news detection.Our findings highlight the importance of a multi-faceted approach,offering a significant improvement in model accuracy and reliability.Moreover,the study emphasizes the adaptability of the proposed ensemble model across diverse datasets,reinforcing its potential for broader application in real-world scenarios.We introduce a pioneering ensemble technique that leverages both machine-learning and deep-learning classifiers.To identify the optimal ensemble configuration,we systematically tested various combinations.Experimental evaluations conducted on three diverse datasets related to fake news demonstrate the exceptional performance of our proposed ensemble model.Achieving remarkable accuracy levels of 97%,99%,and 98%on Dataset 1,Dataset 2,and Dataset 3,respectively,our approach showcases robustness and effectiveness in discerning fake news amidst the complexities of contemporary information landscapes.This research contributes to the advancement of fake news detection methodologies and underscores the significance of integrating feature extraction and feature selection strategies for enhanced performance,especially in the context of intricate,high-dimensional datasets.展开更多
The recent advancements made in World Wide Web and social networking have eased the spread of fake news among people at a faster rate.At most of the times,the intention of fake news is to misinform the people and make...The recent advancements made in World Wide Web and social networking have eased the spread of fake news among people at a faster rate.At most of the times,the intention of fake news is to misinform the people and make manipulated societal insights.The spread of low-quality news in social networking sites has a negative influence upon people as well as the society.In order to overcome the ever-increasing dissemination of fake news,automated detection models are developed using Artificial Intelligence(AI)and Machine Learning(ML)methods.The latest advancements in Deep Learning(DL)models and complex Natural Language Processing(NLP)tasks make the former,a significant solution to achieve Fake News Detection(FND).In this background,the current study focuses on design and development of Natural Language Processing with Sea Turtle Foraging Optimizationbased Deep Learning Technique for Fake News Detection and Classification(STODL-FNDC)model.The aim of the proposed STODL-FNDC model is to discriminate fake news from legitimate news in an effectual manner.In the proposed STODL-FNDC model,the input data primarily undergoes pre-processing and Glove-based word embedding.Besides,STODL-FNDC model employs Deep Belief Network(DBN)approach for detection as well as classification of fake news.Finally,STO algorithm is utilized after adjusting the hyperparameters involved in DBN model,in an optimal manner.The novelty of the study lies in the design of STO algorithm with DBN model for FND.In order to improve the detection performance of STODL-FNDC technique,a series of simulations was carried out on benchmark datasets.The experimental outcomes established the better performance of STODL-FNDC approach over other methods with a maximum accuracy of 95.50%.展开更多
With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature t...With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.展开更多
The evolution of the internet and its accessibility in the twenty-first century has resulted in a tremendous increase in the use of social media platforms.Some social media sources contribute to the propagation of fak...The evolution of the internet and its accessibility in the twenty-first century has resulted in a tremendous increase in the use of social media platforms.Some social media sources contribute to the propagation of fake news that has no real validity,but they accumulate over time and begin to appear in the feed of every consumer producing even more ambiguity.To sustain the value of social media,such stories must be distinguished from the true ones.As a result,an automated system is required to save time and money.The classification of fake news and misinformation from social media data corpora is the subject of this research.Several preprocessing and data improvement procedures are used to gather and preprocess two fake news datasets.Deep text features are extracted using word embedding models Word2vec and Global Vectors for Word representation while textual features are extracted using n-gram approaches named Term Frequency-Inverse Document Frequency and Bag of Words from both datasets individually.Bidirectional Encoder Representations from Transformers(BERT)is also employed to derive embedded representations from the input data.Finally,three Machine Learning(ML)and two Deep Learning(DL)algorithms are utilized for fake news classification.BERT also carries out the classification of embedded outcomes generated by it in parallel with the ML and DL models.In terms of overall performance,the DL-based Convolutional Neural Network stands out in the case of the first while BERT performs better in the case of the second dataset.展开更多
Nowadays,an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication.However,because user-generated content is unregul...Nowadays,an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication.However,because user-generated content is unregulated,it may contain offensive content such as fake news,insults,and harassment phrases.The identification of fake news and rumors and their dissemination on social media has become a critical requirement.They have adverse effects on users,businesses,enterprises,and even political regimes and governments.State of the art has tackled the English language for news and used feature-based algorithms.This paper proposes a model architecture to detect fake news in the Arabic language by using only textual features.Machine learning and deep learning algorithms were used.The deep learning models are used depending on conventional neural nets(CNN),long short-term memory(LSTM),bidirectional LSTM(BiLSTM),CNN+LSTM,and CNN+BiLSTM.Three datasets were used in the experiments,each containing the textual content of Arabic news articles;one of them is reallife data.The results indicate that the BiLSTM model outperforms the other models regarding accuracy rate when both simple data split and recursive training modes are used in the training process.展开更多
Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determ...Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake news.The recently developed machine learning(ML)models can be employed for the detection and classification of fake news.This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and Classification.The goal of the CAS-WELM technique is to discriminate news into fake and real.The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding process.Then,N-gram based feature extraction technique is derived to gen-erate feature vectors.Lastly,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm.The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions.The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.展开更多
The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spr...The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spread data with minimal examination and filters freely.Due to this,the old problem of fake news has resurfaced.It has become an important concern due to its negative impact on the community.To manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)techniques.To perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite effectively.Still,a huge effort is required from the human side to generate the labelled training data.The recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news detection.To unlock social media data,an automatic text classifier is highly helpful in the domain of NLP.The current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)approach.The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news.To attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding process.Besides,the QCLSTM model is utilized for classification.To boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the hyperparameters.The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset.The QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.展开更多
基金supported by the National Key R&D Program of China(Grant No.2022YFB3104601)the Big Data Computing Center of Southeast University.
文摘Social media has significantly accelerated the rapid dissemination of information,but it also boosts propagation of fake news,posing serious challenges to public awareness and social stability.In real-world contexts,the volume of trustable information far exceeds that of rumors,resulting in a class imbalance that leads models to prioritize the majority class during training.This focus diminishes the model’s ability to recognize minority class samples.Furthermore,models may experience overfitting when encountering these minority samples,further compromising their generalization capabilities.Unlike node-level classification tasks,fake news detection in social networks operates on graph-level samples,where traditional interpolation and oversampling methods struggle to effectively generate high-quality graph-level samples.This challenge complicates the identification of new instances of false information.To address this issue,this paper introduces the FHGraph(Fake News Hunting Graph)framework,which employs a generative data augmentation approach and a latent diffusion model to create graph structures that align with news communication patterns.Using the few-sample learning capabilities of large language models(LLMs),the framework generates diverse texts for minority class nodes.FHGraph comprises a hierarchical multiview graph contrastive learning module,in which two horizontal views and three vertical levels are utilized for self-supervised learning,resulting in more optimized representations.Experimental results show that FHGraph significantly outperforms state-of-the-art(SOTA)graph-level class imbalance methods and SOTA graph-level contrastive learning methods.Specifically,FHGraph has achieved a 2%increase in F1 Micro and a 2.5%increase in F1 Macro in the PHEME dataset,as well as a 3.5%improvement in F1 Micro and a 4.3%improvement in F1 Macro on RumorEval dataset.
基金funded by Umm Al-Qura University,Saudi Arabia under grant number:25UQU4300346GSSR05.
文摘These days,social media has grown to be an integral part of people’s lives.However,it involves the possibility of exposure to“fake news”,which may contain information that is intentionally or inaccurately false to promote particular political or economic interests.The main objective of this work is to use the co-attention mechanism in a Combined Graph neural network model(CMCG)to capture the relationship between user profile features and user preferences in order to detect fake news and examine the influence of various social media features on fake news detection.The proposed approach includes three modules.The first one creates a Graph Neural Network(GNN)based model to learn user profile properties,while the second module encodes news content,user historical posts,and news sharing cascading on social media as user preferences GNN-based model.The inter-dependencies between user profiles and user preferences are handled through the third module using a co-attention mechanism for capturing the relationship between the two GNN-based models.We conducted several experiments on two commonly used fake news datasets,Politifact and Gossipcop,where our approach achieved 98.53%accuracy on the Gossipcop dataset and 96.77%accuracy on the Politifact dataset.These results illustrate the effectiveness of the CMCG approach for fake news detection,as it combines various information from different modalities to achieve relatively high performances.
基金supported by Communication University of China(HG23035)partly supported by the Fundamental Research Funds for the Central Universities(CUC230A013).
文摘With the rapid growth of socialmedia,the spread of fake news has become a growing problem,misleading the public and causing significant harm.As social media content is often composed of both images and text,the use of multimodal approaches for fake news detection has gained significant attention.To solve the problems existing in previous multi-modal fake news detection algorithms,such as insufficient feature extraction and insufficient use of semantic relations between modes,this paper proposes the MFFFND-Co(Multimodal Feature Fusion Fake News Detection with Co-Attention Block)model.First,the model deeply explores the textual content,image content,and frequency domain features.Then,it employs a Co-Attention mechanism for cross-modal fusion.Additionally,a semantic consistency detectionmodule is designed to quantify semantic deviations,thereby enhancing the performance of fake news detection.Experimentally verified on two commonly used datasets,Twitter and Weibo,the model achieved F1 scores of 90.0% and 94.0%,respectively,significantly outperforming the pre-modified MFFFND(Multimodal Feature Fusion Fake News Detection with Attention Block)model and surpassing other baseline models.This improves the accuracy of detecting fake information in artificial intelligence detection and engineering software detection.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant Number(120/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR32).
文摘Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.
基金the National Natural Science Foundation of China(No.62302540)with author F.F.S.For more information,please visit their website at https://www.nsfc.gov.cn/.Additionally,it is also funded by the Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(No.HNTS2022020)+1 种基金where F.F.S is an author.Further details can be found at http://xt.hnkjt.gov.cn/data/pingtai/.The research is also supported by the Natural Science Foundation of Henan Province Youth Science Fund Project(No.232300420422)for more information,you can visit https://kjt.henan.gov.cn/2022/09-02/2599082.html.Lastly,it receives funding from the Natural Science Foundation of Zhongyuan University of Technology(No.K2023QN018),where F.F.S is an author.You can find more information at https://www.zut.edu.cn/.
文摘As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensively represent themultifaceted informationof fake news.
文摘In recent years,social media platforms have gained immense popularity.As a result,there has been a tremendous increase in content on social media platforms.This content can be related to an individual’s sentiments,thoughts,stories,advertisements,and news,among many other content types.With the recent increase in online content,the importance of identifying fake and real news has increased.Although,there is a lot of work present to detect fake news,a study on Fuzzy CRNN was not explored into this direction.In this work,a system is designed to classify fake and real news using fuzzy logic.The initial feature extraction process is done using a convolutional recurrent neural network(CRNN).After the extraction of features,word indexing is done with high dimensionality.Then,based on the indexing measures,the ranking process identifies whether news is fake or real.The fuzzy CRNN model is trained to yield outstanding resultswith 99.99±0.01%accuracy.This work utilizes three different datasets(LIAR,LIAR-PLUS,and ISOT)to find the most accurate model.
文摘In view of the various adverse effects,fake news detection has become an extremely important task.So far,many detection methods have been proposed,but these methods still have some limitations.For example,only two independently encoded unimodal information are concatenated together,but not integrated with multimodal information to complete the complementary information,and to obtain the correlated information in the news content.This simple fusion approach may lead to the omission of some information and bring some interference to the model.To solve the above problems,this paper proposes the FakeNewsDetectionmodel based on BLIP(FNDB).First,the XLNet and VGG-19 based feature extractors are used to extract textual and visual feature representation respectively,and BLIP based multimodal feature extractor to obtain multimodal feature representation in news content.Then,the feature fusion layer will fuse these features with the help of the cross-modal attention module to promote various modal feature representations for information complementation.The fake news detector uses these fused features to identify the input content,and finally complete fake news detection.Based on this design,FNDB can extract as much information as possible from the news content and fuse the information between multiple modalities effectively.The fake news detector in the FNDB can also learn more information to achieve better performance.The verification experiments on Weibo and Gossipcop,two widely used real-world datasets,show that FNDB is 4.4%and 0.6%higher in accuracy than the state-of-theart fake news detection methods,respectively.
基金Abu Dhabi University’s Office of sponsored programs in the United Arab Emirates(Grant Number:19300752)funded this endeavor.
文摘The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social media.Individuals can quickly fabricate comments and news on social media.The most difficult challenge is determining which news is real or fake.Accordingly,tracking down programmed techniques to recognize fake news online is imperative.With an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media content.This study shows past,current,and possible methods that can be used in the future for fake news classification.Two different publicly available datasets containing political news are utilized for performing experiments.Sixteen supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text classification.In contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer text.Additionally,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.
文摘In the past few years,social media and online news platforms have played an essential role in distributing news content rapidly.Consequently.verification of the authenticity of news has become a major challenge.During the COVID-19 outbreak,misinformation and fake news were major sources of confusion and insecurity among the general public.In the first quarter of the year 2020,around 800 people died due to fake news relevant to COVID-19.The major goal of this research was to discover the best learning model for achieving high accuracy and performance.A novel case study of the Fake News Classification using ELECTRA model,which achieved 85.11%accuracy score,is thus reported in this manuscript.In addition to that,a new novel dataset called COVAX-Reality containing COVID-19 vaccine-related news has been contributed.Using the COVAX-Reality dataset,the performance of FNEC is compared to several traditional learning models i.e.,Support Vector Machine(SVM),Naive Bayes(NB),Passive Aggressive Classifier(PAC),Long Short-Term Memory(LSTM),Bi-directional LSTM(Bi-LSTM)and Bi-directional Encoder Representations from Transformers(BERT).For the evaluation of FNEC,standard metrics(Precision,Recall,Accuracy,and F1-Score)were utilized.
文摘This paper proposes an original behavioural finance representative agent model,to explain how fake news’empirical price impacts can persist in finance despite contradicting the efficient-market hypothesis.The model reconciles empirically-observed price overreactions to fake news with empirically-observed price underreactions to real news,and predicts a novel secondary impact of fake news:that fake news in a security amplifies underreactions to subsequent real news for the security.Evaluating the model against a large-sample event study of the 2019 Chinese ADR Delisting Threat fake news and debunking event,this paper finds strong qualitative validation for its model’s dynamics and predictions.
基金This research was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF2020R1I1A3073313).
文摘Following the development of communication techniques and smart devices,the era of Artificial Intelligence(AI)and big data has arrived.The increased connectivity,referred to as hyper-connectivity,has led to the development of smart cities.People in these smart cities can access numerous online contents and are always connected.These developments,however,also lead to a lack of standardization and consistency in the propagation of information throughout communities due to the consumption of information through social media channels.Information cannot often be verified,which can confuse the users.The increasing influence of social media has thus led to the emergence and increasing prevalence of fake news.In this study,we propose a methodology to classify and identify fake news emanating from social channels.We collected content from Twitter to detect fake news and statistically verified that the temporal propagation pattern of quote retweets is effective for the classification of fake news.To verify this,we trained the temporal propagation pattern to a two-phases deep learning model based on convolutional neural networks and long short-term memory.The fake news classifier demonstrates the ability for its early detection.Moreover,it was verified that the temporal propagation pattern was the most influential feature compared to other feature groups discussed in this paper.
基金supported by the National Natural Science Foundation of China(No.62302540)with author Fangfang Shan.For more information,please visit their website at https://www.nsfc.gov.cn/(accessed on 31/05/2024)+3 种基金Additionally,it is also funded by the Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(No.HNTS2022020)where Fangfang Shan is an author.Further details can be found at http://xt.hnkjt.gov.cn/data/pingtai/(accessed on 31/05/2024)supported by the Natural Science Foundation of Henan Province Youth Science Fund Project(No.232300420422)for more information,you can visit https://kjt.henan.gov.cn/2022/09-02/2599082.html(accessed on 31/05/2024).
文摘Social media has become increasingly significant in modern society,but it has also turned into a breeding ground for the propagation of misleading information,potentially causing a detrimental impact on public opinion and daily life.Compared to pure text content,multmodal content significantly increases the visibility and share ability of posts.This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection.To effectively address the critical challenge of accurately detecting fake news on social media,this paper proposes a fake news detection model based on crossmodal message aggregation and a gated fusion network(MAGF).MAGF first uses BERT to extract cumulative textual feature representations and word-level features,applies Faster Region-based ConvolutionalNeuralNetwork(Faster R-CNN)to obtain image objects,and leverages ResNet-50 and Visual Geometry Group-19(VGG-19)to obtain image region features and global features.The image region features and word-level text features are then projected into a low-dimensional space to calculate a text-image affinity matrix for cross-modal message aggregation.The gated fusion network combines text and image region features to obtain adaptively aggregated features.The interaction matrix is derived through an attention mechanism and further integrated with global image features using a co-attention mechanism to producemultimodal representations.Finally,these fused features are fed into a classifier for news categorization.Experiments were conducted on two public datasets,Twitter and Weibo.Results show that the proposed model achieves accuracy rates of 91.8%and 88.7%on the two datasets,respectively,significantly outperforming traditional unimodal and existing multimodal models.
基金This research was funded by the General Project of Philosophy and Social Science of Heilongjiang Province,Grant Number:20SHB080.
文摘In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical.Unfortunately,existing approaches fail to handle these problems.This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues(TD-MMC),which utilizes three valuable multi-model clues:text-model importance,text-image complementary,and text-image inconsistency.TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation.To reduce the irrelevant social structure’s information interference,we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features.A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information.In addition,TD-MMC employs a new multi-model loss to improve the model’s generalization ability.Extensive experiments have been conducted on two public real-world English and Chinese datasets,and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ICT Creative Consilience Program(IITP-2024-2020-0-01819)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of distinguishing between truthful and deceptive information.Fake news,a prevalent issue,particularly on social media,complicates the assessment of news credibility.The pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources,creating confusion and polarizing opinions.As the volume of information grows,individuals increasingly struggle to discern credible content from false narratives,leading to widespread misinformation and potentially harmful consequences.Despite numerous methodologies proposed for fake news detection,including knowledge-based,language-based,and machine-learning approaches,their efficacy often diminishes when confronted with high-dimensional datasets and data riddled with noise or inconsistencies.Our study addresses this challenge by evaluating the synergistic benefits of combining feature extraction and feature selection techniques in fake news detection.We employ multiple feature extraction methods,including Count Vectorizer,Bag of Words,Global Vectors for Word Representation(GloVe),Word to Vector(Word2Vec),and Term Frequency-Inverse Document Frequency(TF-IDF),alongside feature selection techniques such as Information Gain,Chi-Square,Principal Component Analysis(PCA),and Document Frequency.This comprehensive approach enhances the model’s ability to identify and analyze relevant features,leading to more accurate and effective fake news detection.Our findings highlight the importance of a multi-faceted approach,offering a significant improvement in model accuracy and reliability.Moreover,the study emphasizes the adaptability of the proposed ensemble model across diverse datasets,reinforcing its potential for broader application in real-world scenarios.We introduce a pioneering ensemble technique that leverages both machine-learning and deep-learning classifiers.To identify the optimal ensemble configuration,we systematically tested various combinations.Experimental evaluations conducted on three diverse datasets related to fake news demonstrate the exceptional performance of our proposed ensemble model.Achieving remarkable accuracy levels of 97%,99%,and 98%on Dataset 1,Dataset 2,and Dataset 3,respectively,our approach showcases robustness and effectiveness in discerning fake news amidst the complexities of contemporary information landscapes.This research contributes to the advancement of fake news detection methodologies and underscores the significance of integrating feature extraction and feature selection strategies for enhanced performance,especially in the context of intricate,high-dimensional datasets.
文摘The recent advancements made in World Wide Web and social networking have eased the spread of fake news among people at a faster rate.At most of the times,the intention of fake news is to misinform the people and make manipulated societal insights.The spread of low-quality news in social networking sites has a negative influence upon people as well as the society.In order to overcome the ever-increasing dissemination of fake news,automated detection models are developed using Artificial Intelligence(AI)and Machine Learning(ML)methods.The latest advancements in Deep Learning(DL)models and complex Natural Language Processing(NLP)tasks make the former,a significant solution to achieve Fake News Detection(FND).In this background,the current study focuses on design and development of Natural Language Processing with Sea Turtle Foraging Optimizationbased Deep Learning Technique for Fake News Detection and Classification(STODL-FNDC)model.The aim of the proposed STODL-FNDC model is to discriminate fake news from legitimate news in an effectual manner.In the proposed STODL-FNDC model,the input data primarily undergoes pre-processing and Glove-based word embedding.Besides,STODL-FNDC model employs Deep Belief Network(DBN)approach for detection as well as classification of fake news.Finally,STO algorithm is utilized after adjusting the hyperparameters involved in DBN model,in an optimal manner.The novelty of the study lies in the design of STO algorithm with DBN model for FND.In order to improve the detection performance of STODL-FNDC technique,a series of simulations was carried out on benchmark datasets.The experimental outcomes established the better performance of STODL-FNDC approach over other methods with a maximum accuracy of 95.50%.
基金The authors are highly thankful to the National Social Science Foundation of China(20BXW101,18XXW015)Innovation Research Project for the Cultivation of High-Level Scientific and Technological Talents(Top-Notch Talents of theDiscipline)(ZZKY2022303)+3 种基金National Natural Science Foundation of China(Nos.62102451,62202496)Basic Frontier Innovation Project of Engineering University of People’s Armed Police(WJX202316)This work is also supported by National Natural Science Foundation of China(No.62172436)Engineering University of PAP’s Funding for Scientific Research Innovation Team,Engineering University of PAP’s Funding for Basic Scientific Research,and Engineering University of PAP’s Funding for Education and Teaching.Natural Science Foundation of Shaanxi Province(No.2023-JCYB-584).
文摘With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.
文摘The evolution of the internet and its accessibility in the twenty-first century has resulted in a tremendous increase in the use of social media platforms.Some social media sources contribute to the propagation of fake news that has no real validity,but they accumulate over time and begin to appear in the feed of every consumer producing even more ambiguity.To sustain the value of social media,such stories must be distinguished from the true ones.As a result,an automated system is required to save time and money.The classification of fake news and misinformation from social media data corpora is the subject of this research.Several preprocessing and data improvement procedures are used to gather and preprocess two fake news datasets.Deep text features are extracted using word embedding models Word2vec and Global Vectors for Word representation while textual features are extracted using n-gram approaches named Term Frequency-Inverse Document Frequency and Bag of Words from both datasets individually.Bidirectional Encoder Representations from Transformers(BERT)is also employed to derive embedded representations from the input data.Finally,three Machine Learning(ML)and two Deep Learning(DL)algorithms are utilized for fake news classification.BERT also carries out the classification of embedded outcomes generated by it in parallel with the ML and DL models.In terms of overall performance,the DL-based Convolutional Neural Network stands out in the case of the first while BERT performs better in the case of the second dataset.
文摘Nowadays,an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication.However,because user-generated content is unregulated,it may contain offensive content such as fake news,insults,and harassment phrases.The identification of fake news and rumors and their dissemination on social media has become a critical requirement.They have adverse effects on users,businesses,enterprises,and even political regimes and governments.State of the art has tackled the English language for news and used feature-based algorithms.This paper proposes a model architecture to detect fake news in the Arabic language by using only textual features.Machine learning and deep learning algorithms were used.The deep learning models are used depending on conventional neural nets(CNN),long short-term memory(LSTM),bidirectional LSTM(BiLSTM),CNN+LSTM,and CNN+BiLSTM.Three datasets were used in the experiments,each containing the textual content of Arabic news articles;one of them is reallife data.The results indicate that the BiLSTM model outperforms the other models regarding accuracy rate when both simple data split and recursive training modes are used in the training process.
基金This research was supported by the Researchers Supporting Program(TUMA-Project2021-27)Almaarefa UniversityRiyadh,Saudi Arabia.Taif University Researchers Supporting Project number(TURSP-2020/161)Taif University,Taif,Saudi Arabia.
文摘Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake news.The recently developed machine learning(ML)models can be employed for the detection and classification of fake news.This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and Classification.The goal of the CAS-WELM technique is to discriminate news into fake and real.The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding process.Then,N-gram based feature extraction technique is derived to gen-erate feature vectors.Lastly,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm.The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions.The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R281)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR41).
文摘The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spread data with minimal examination and filters freely.Due to this,the old problem of fake news has resurfaced.It has become an important concern due to its negative impact on the community.To manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)techniques.To perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite effectively.Still,a huge effort is required from the human side to generate the labelled training data.The recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news detection.To unlock social media data,an automatic text classifier is highly helpful in the domain of NLP.The current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)approach.The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news.To attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding process.Besides,the QCLSTM model is utilized for classification.To boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the hyperparameters.The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset.The QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.
