Multimodal Social Media Fake News Detection Based on Similarity Inference and Adversarial Networks

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.

Fake News Detection Based on Cross-Modal Message Aggregation and Gated Fusion Network

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.

Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

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.

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

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.

Fake News Detection Based on Multimodal Inputs

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.

Arabic Fake News Detection Using Deep Learning

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.

Cross-Modal Relation-Aware Networks for Fake News Detection

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.

A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

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.

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

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 research and modeling are shifting toward the use of ensemble approaches.Originality/value-The study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful,although currently reported accuracy has not yet reached consistent levels in the real world.

Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

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.

Classification and Detection of Amharic Language Fake News on Social Media Using Machine Learning Approach

The pervasive idea of web-based media stages brought about a lot of sight and sound information in interpersonal organizations.The transparency and unlimited way of sharing the data via online media stage encourages data spread across the organization paying little mind to its noteworthiness.The multiplication of misdirecting data in regular access news sources,for example,web-based media channels,news websites,and online papers has made it trying to recognize dependable news sources,in this way expanding the requirement for computational devices to give bits of knowledge into the unwavering quality of online substance.The broad spread of phony news contrarily affects people and society.Along these lines,counterfeit news identification via web-based media has as of late become arising research drawing in enormous consideration.Observing the possible damage caused by the rapid spread of fake news in various fields such as politics and finance,the use of language analysis to automatically identify fake news has attracted the attention of the research community.A social networking service is a platform for people with similar interests,activities,or backgrounds to form social networks or social relations.Participants who register on this site with its own expression(often a profile)and social links are generally offered a social network service.

Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

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.

Fake News Classification Using a Fuzzy Convolutional Recurrent Neural Network

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.

Natural Language Processing with Optimal Deep Learning Based Fake News Classification

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%.

News Topic Detection Based on Capsule Semantic Graph

Most news topic detection methods use word-based methods,which easily ignore the relationship among words and have semantic sparsity,resulting in low topic detection accuracy.In addition,the current mainstream probability methods and graph analysis methods for topic detection have high time complexity.For these reasons,we present a news topic detection model on the basis of capsule semantic graph(CSG).The keywords that appear in each text at the same time are modeled as a keyword graph,which is divided into multiple subgraphs through community detection.Each subgraph contains a group of closely related keywords.The graph is used as the vertex of CSG.The semantic relationship among the vertices is obtained by calculating the similarity of the average word vector of each vertex.At the same time,the news text is clustered using the incremental clustering method,where each text uses CSG;that is,the similarity among texts is calculated by the graph kernel.The relationship between vertices and edges is also considered when calculating the similarity.Experimental results on three standard datasets show that CSG can obtain higher precision,recall,and F1 values than several latest methods.Experimental results on large-scale news datasets reveal that the time complexity of CSG is lower than that of probabilistic methods and other graph analysis methods.

Modeling Chinese Microblogs with Five Ws for Topic Hashtags Extraction

Hashtags are important metadata in microblogs and are used to mark topics or index messages. However,statistics show that hashtags are absent from most microblogs. This poses great challenges for the retrieval and analysis of these tagless microblogs. In this paper, we summarize the similarity between microblogs and shortmessage-style news, and then propose an algorithm, named 5WTAG, for detecting microblog topics based on a model of five Ws（When, Where, Who, What, ho W）. As five-W attributes are the core components in event description, it is guaranteed theoretically that 5WTAG can properly extract semantic topics from microblogs. We introduce the detailed procedure of the algorithm in this paper including spam microblog identification, microblog segmentation, and candidate hashtag construction. In addition, we propose a novel recommendation computing method for ranking candidate hashtags, which combines syntax and semantic analysis and observes the distribution of artificial topic hashtags. Finally, we conduct comprehensive experiments to verify the semantic correctness and completeness of the candidate hashtags, as well as the accuracy of the recommendation method using real data from Sina Weibo.