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Multimodal Social Media fake news detection Based on Similarity Inference and Adversarial Networks

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Computers, Materials & Continua 2024年第4期79卷 581-605页

作者： Fangfang Shan Huifang Sun Mengyi Wang College of Computer Zhongyuan University of TechnologyZhengzhou450007China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou 450001China

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 comprehensiv

关键词： fake news detection attention mechanism image-text similarity multimodal feature fusion

fake news detection Based on Multimodal Inputs

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Computers, Materials & Continua 2023年第5期75卷 4519-4534页

作者： Zhiping Liang Unversity of Melbourne MelbourneVIC 3010Australia

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.

关键词： Natural language processing fake news detection machine learning text classification

Hunter Prey Optimization with Hybrid Deep Learning for fake news detection on Arabic Corpus

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Computers, Materials & Continua 2023年第5期75卷 4255-4272页

作者： Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Khaled Tarmissi Amal S.Mehanna Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityMuhayil63763Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

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.

关键词： Arabic corpus fake news detection deep learning hunter prey optimizer classification model

Arabic fake news detection Using Deep Learning

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Computers, Materials & Continua 2022年第5期71卷 3647-3665页

作者： Khaled M.Fouad Sahar F.Sabbeh Walaa Medhat Faculty of Computers&Artificial Intelligence Benha UniversityEgypt University of Jeddah College of Computer Science and EngineeringJeddah21493Saudi Arabia Information Technology and Computer Science Nile UniversityEgypt

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.

关键词： fake news detection deep learning machine learning natural language processing

fake news detection Using Machine Learning and Knowledge Graph

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Fake News Detection Using Machine Learning and Knowledge Gra...

作者： Khawaja Rokham Tariq 北京邮电大学

学位级别：硕士

In today's digital era,fake news presents serious problems.It rapidly spreads over social media,plays on confirmation bias,and undermines the credibility of reputable news publishers.Machine learning(ML)refers to the systematic exploration of algorithms and statistical models,while deep learning enables the design of computational models that consist of numerous layers of processing that can acquire abstract representations of data at various levels.1)This study focuses on different approaches to machine learning and compares different models to evaluate and analyze their accuracy.Subsequently,a hybrid model is proposed,featuring a nine-layer architecture to achieve accurate binary sequence classification.The use of various layers allows for effective feature extraction,dependency capture,and attention focusing,while dropout regularization prevents over-fitting.The proposed model has achieved an accuracy of 97.4%on a large dataset,which outperforms existing models like LSTM and CNN.2)Secondly,a knowledge graph is constructed to visualize fake news detection results.The knowledge graph categorizes news items as true or false using labels 0 and 1,respectively.Instead of creating separate nodes,our knowledge graph employs a linking approach for enabling the examination of dissemination patterns and the identification of publishers which publish the two types of news.3)Finally,a recommendation system is developed to recommend the news detection results to the readers.The proposed hybrid model is employed within the recommendation system.If the news is true,the recommendation system automatically recommends the reader to go to the website for further reading;otherwise,if the news is false,it does not encourage the reader to read it.

关键词： fake news detection machine learning deep learning knowledge graph recommendation system

A systematic survey on deep learning and machine learning approaches of fake news detection in the pre-and post-COVID-19 pandemic

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International Journal of Intelligent Computing and Cybernetics 2021年第4期14卷 617-646页

作者： Rajshree Varma Yugandhara Verma Priya Vijayvargiya Prathamesh P.Churi Computer Engineering Narsee Monjee Institute of Management and Higher StudiesMumbaiIndia

Purpose-The rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels,freelance reporters and websites.Amid the coronavirus disease 2019(COVID-19)pandemic,individuals are inflicted with these false and potentially harmful claims and stories,which may harm the vaccination process.Psychological studies reveal that the human ability to detect deception is only slightly better than chance;therefore,there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate.This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre-and post-pandemic,which has never been done before to the best of the authors’knowledge.Design/methodology/approach-The detailed literature review on fake news detection is divided into three major parts.The authors searched papers no later than 2017 on fake news detection approaches on deep learning andmachine learning.The paperswere initially searched through theGoogle scholar platform,and they have been scrutinized for quality.The authors kept“Scopus”and“Web of Science”as quality indexing parameters.All research gaps and available databases,data pre-processing,feature extraction techniques and evaluationmethods for current fake news detection technologies have been explored,illustrating them using tables,charts and trees.Findings-The paper is dissected into two approaches,namely machine learning and deep learning,to present a better understanding and a clear objective.Next,the authors present a viewpoint on which approach is better and future research trends,issues and challenges for researchers,given the relevance and urgency of a detailed and thorough analysis of existing models.This paper also delves into fake new detection during COVID-19,and it can be inferred that

关键词： fake news detection Machine learning Deep learning Artificial intelligence Natural language processing COVID-19 Social media Misinformation

Cross-Modal Relation-Aware Networks for fake news detection

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Journal of New Media 2022年第1期4卷 13-26页

作者： Hui Yu Jinguang Wang School of Computer&Software Nanjing University of Information Science and TechnologyNanjing210044China

With the speedy development of communication Internet and the widespread use of social multimedia,so many creators have published posts on social multimedia platforms that fake news detection has already been a challenging task.Although some works use deep learning methods to capture visual and textual information of posts,most existingmethods cannot explicitly model the binary relations among image regions or text tokens to mine the global relation information in a modality deeply such as image or text.Moreover,they cannot fully exploit the supplementary cross-modal information,including image and text relations,to supplement and enrich each modality.In order to address these problems,in this paper,we propose an innovative end-to-end Cross-modal Relation-aware Networks(CRAN),which exploits jointly models the visual and textual information with their corresponding relations in a unified framework.(1)To capture the global structural relations in a modality,we design a global relation-aware network to explicitly model the relation-aware semantics of the fragment features in the target modality from a global scope perspective.(2)To effectively fuse cross-modal information,we propose a cross-modal co-attention network module for multi-modal information fusion,which utilizes the intra-modality relationships and inter-modality relationship jointly among image regions and textual words to replenish and heighten each other.Extensive experiments on two public real-world datasets demonstrate the superior performance of CRAN compared with other state-of-the-art baseline algorithms.

关键词： fake news detection relation-aware networks multi-modal fusion

fake news Classification Using a Fuzzy Convolutional Recurrent Neural Network

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Computers, Materials & Continua 2022年第6期71卷 5733-5750页

作者： Dheeraj Kumar Dixit Amit Bhagat Dharmendra Dangi Department of Mathematics and Computer Applications Maulana Azad National Institute of Technology(MANIT)BhopalMadhya PradeshIndia

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.

关键词： fake news detection text classification convolution recurrent neural network fuzzy convolutional recurrent neural networks

Natural Language Processing with Optimal Deep Learning Based fake news Classification

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Computers, Materials & Continua 2022年第11期73卷 3529-3544页

作者： Sara AAlthubiti Fayadh Alenezi Romany F.Mansour Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

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%.

关键词： Natural language processing text mining fake news detection deep belief network machine learning evolutionary algorithm