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Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-modal Fake News Detection [article]

Jinyin Chen, Chengyu Jia, Haibin Zheng, Ruoxi Chen, Chenbo Fu
2022 arXiv   pre-print
The proliferation of fake news and its serious negative social influence push fake news detection methods to become necessary tools for web managers.  ...  Meanwhile, the multi-media nature of social media makes multi-modal fake news detection popular for its ability to capture more modal features than uni-modal detection methods.  ...  To better illustrate the robustness of the current dominant multi-modal fake news detectors (attention-based recurrent neural network (Att-RNN) [14] , event adversarial neural networks (EANN) [3] , multi-modal  ... 
arXiv:2206.08788v1 fatcat:2e5fqhiirvh2nm3r24l7ronrsu

Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities [article]

Sara Abdali
2022 arXiv   pre-print
field of multi-modal misinformation detection.  ...  Thus, many research efforts have been put into development of automatic techniques for detecting possible cross-modal discordances in web-based media.  ...  propose Event Adversarial Neural Networks (EANN) [83] , an end-to-end framework which can derive event-invariant features and thus benefit the detection of fake news on newly arrived events.  ... 
arXiv:2203.13883v3 fatcat:ari4onbo45ejfnwdnjgdti5daq

Fake News Detection via Multi-modal Topic Memory Network

Long Ying, Hui Yu, Jinguang Wang, Yongze Ji, Shengsheng Qian
2021 IEEE Access  
INDEX TERMS Fake news detection, multi-modal fusion, topic memory network, blended attention module.  ...  With the development of the Mobile Internet, more and more people create and release multimodal posts on social media platforms. Fake news detection has become an increasingly challenging task.  ...  [13] design an event adversarial neural network (EANN) learning event-invariant features to obtain the multi-modal features of each post for fake news detection.  ... 
doi:10.1109/access.2021.3113981 fatcat:qltfyne4bjd5rnxyqtfs3vneta

Predicting image credibility in fake news over social media using multi-modal approach

Bhuvanesh Singh, Dilip Kumar Sharma
2021 Neural computing & applications (Print)  
The proposed framework utilizes explicit convolution neural network model EfficientNetB0 for images and sentence transformer for text analysis.  ...  In this paper, an efficient multi-modal approach is proposed, which detects fake images of microblogging platforms. No further additional subcomponents are required.  ...  [38] proposed EANN [event adversarial neural networks] to detect fake news, that obtain eventinvariant characteristics, and assist fake detection on newly emerged events.  ... 
doi:10.1007/s00521-021-06086-4 pmid:34054227 pmcid:PMC8143443 fatcat:h2uxvbdyljgxhgneaidvzdle6i

Multi-level Multi-modal Cross-attention Network for Fake News Detection

Long Ying, Hui Yu, Jinguang Wang, Yongze Ji, Shengsheng Qian
2021 IEEE Access  
INDEX TERMS Multi-level Neural Networks; Fake News Detection; Multi-modal Fusion I.  ...  erate the multi-modal features of every post for fake news For single-modality analysis, most existing methods [3], detection by using a novel adversarial network  ... 
doi:10.1109/access.2021.3114093 fatcat:l4dscphtunblfd6hsauzwm6ima

A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks [article]

Chahat Raj, Priyanka Meel
2021 arXiv   pre-print
This review primarily deals with multi-modal fake news detection techniques that include images, videos, and their combinations with text.  ...  The combination of various modalities has resulted in efficient fake news detection. At present, there is an abundance of surveys consolidating textual fake news detection algorithms.  ...  This Event Adversarial Neural Network (EANN) framework can handle event-invariant features, thus allowing the detection of fake news on freshly arriving events.  ... 
arXiv:2112.00803v1 fatcat:twppg5v37bdozcdloaa6zfk7s4

FNR: A Similarity and Transformer-Based Approachto Detect Multi-Modal FakeNews in Social Media [article]

Faeze Ghorbanpour, Maryam Ramezani, Mohammad A. Fazli, Hamid R. Rabiee
2021 arXiv   pre-print
Therefore, verifying social media news and spotting fakes is crucial. This work aims to analyze multi-modal features from texts and images in social media for detecting fake news.  ...  The results show the proposed method achieves higher accuracies in detecting fake news compared to the previous works.  ...  Multiple Modality The authors in [11] employed a visual system to answer questions via deep networks for fake news detection using multi-modal data.  ... 
arXiv:2112.01131v1 fatcat:mdnofls76jbdnakq7gu2jcargu

SAFE: Similarity-Aware Multi-Modal Fake News Detection [article]

Xinyi Zhou, Jindi Wu, Reza Zafarani
2020 arXiv   pre-print
In this work, we propose a Similarity-Aware FakE news detection method (SAFE) which investigates multi-modal (textual and visual) information of news articles.  ...  First, neural networks are adopted to separately extract textual and visual features for news representation. We further investigate the relationship between the extracted features across modalities.  ...  Wang et al. proposed Event Adversarial Neural Network (EANN) to learn event-invariant features representative of news content across various topics and domains [23] .  ... 
arXiv:2003.04981v1 fatcat:anspchz3vjaybeztvan7sgytzy

$$\mathsf {SAFE}$$: Similarity-Aware Multi-modal Fake News Detection [chapter]

Xinyi Zhou, Jindi Wu, Reza Zafarani
2020 Lecture Notes in Computer Science  
In this work, we propose a Similarity-Aware FakE news detection method (SAFE) which investigates multi-modal (textual and visual) information of news articles.  ...  First, neural networks are adopted to separately extract textual and visual features for news representation. We further investigate the relationship between the extracted features across modalities.  ...  Wang et al. proposed Event Adversarial Neural Network (EANN) to learn event-invariant features representative of news content across various topics and domains [23] .  ... 
doi:10.1007/978-3-030-47436-2_27 fatcat:yhuj6ha24bempk2hnvdfv7thpu

Research status of deep learning methods for rumor detection

Li Tan, Ge Wang, Feiyang Jia, Xiaofeng Lian
2022 Multimedia tools and applications  
Besides, this work summarizes 30 works into 7 rumor detection methods such as propagation trees, adversarial learning, cross-domain methods, multi-task learning, unsupervised and semi-supervised methods  ...  Many studies used methods of deep learning to detect rumors in open networks.  ...  Wang et al. (2018) to conduct cross-domain event fake news detection work for multi-modal data and introduced the multi-modal separation representation learning in study of Y.  ... 
doi:10.1007/s11042-022-12800-8 pmid:35469150 pmcid:PMC9022167 fatcat:h5vjukpkyzdhnjhikgtpj347e4

SEMI-FND: Stacked Ensemble Based Multimodal Inference For Faster Fake News Detection [article]

Prabhav Singh, Ridam Srivastava, K.P.S. Rana, Vineet Kumar
2022 arXiv   pre-print
Fake News Detection (FND) is an essential field in natural language processing that aims to identify and check the truthfulness of major claims in a news article to decide the news veracity.  ...  Further, with the explosive rise in fake news dissemination over social media, including images and text, it has become imperative to identify fake news faster and more accurately.  ...  [23] proposed an endto-end framework of adversarial neural networks to derive event-invariant features and thus facilitate the detection of fake news on newly arrived events.  ... 
arXiv:2205.08159v1 fatcat:dukawgay6fcm5hs45a2jklvd2i

Multimodal Fusion with BERT and Attention Mechanism for Fake News Detection [article]

Nguyen Manh Duc Tuan, Pham Quang Nhat Minh
2021 arXiv   pre-print
In this paper, we present a novel method for detecting fake news by fusing multimodal features derived from textual and visual data.  ...  Fake news detection is an important task for increasing the credibility of information on the media since fake news is constantly spreading on social media every day and it is a very serious concern in  ...  [26] built an end-to-end model called Event Adversarial Neural Networks for Multi-Modal Fake News Detection (EANN). That model has two components: event discriminator and fake news classification.  ... 
arXiv:2104.11476v2 fatcat:cgsejtbhvbcfbjcofvsllftijm

Multimodal Multi-image Fake News Detection

Anastasia Giachanou, Guobiao Zhang, Paolo Rosso
2020 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)  
It is possible to detect fake news using only the multi-modal feature extractor and the fake news detector.  ...  [26] proposed the Event Adversarial Neural Networks (EANN) model that consists of two components: the textual and the visual.  ... 
doi:10.1109/dsaa49011.2020.00091 dblp:conf/dsaa/GiachanouZR20 fatcat:lgp5o6odcjc2vn5ovnc5z2v6tm

Veracity Assessment of Multimedia Facebook Posts for Infodemic Symptom Detection using Bi-modal Unsupervised Machine Learning Approach

Taiwo Olapeju Olaleye
2021 International Journal for Research in Applied Science and Engineering Technology  
Veracity assessment of polarized opinions expressed in negative clusters reveals that provocative, derogatory, obnoxious, etc. indicate propensity for infodemic tendencies. Keywords: Fake news.  ...  Using a multimedia facebook corpus, an unsupervised natural language processor, Inception v3 model, coupled with a hierarchical clustering network, is deployed for the duo of image and text sentiment analytics  ...  (Qi, Cao, Yang, Guo, & Li, 2019), a multi-domain visual neural network model to fuse the pictorial information of frequency and pixel domain for fake news detection is presented.  ... 
doi:10.22214/ijraset.2021.39406 fatcat:6szeibvvincrfnpxkrs5m5o6m4

Exploiting Multi-domain Visual Information for Fake News Detection [article]

Peng Qi, Juan Cao, Tianyun Yang, Junbo Guo, Jintao Li
2019 arXiv   pre-print
Therefore, we propose a novel framework Multi-domain Visual Neural Network (MVNN) to fuse the visual information of frequency and pixel domains for detecting fake news.  ...  Hence, how to fully exploit the inherent characteristics of fake-news images is an important but challenging problem for fake news detection.  ...  to detect newlyemerged fake news events based on multi-modal features; [18] presents a novel approach to learn a shared representation of multimodal information for fake news detection.  ... 
arXiv:1908.04472v1 fatcat:rb464rcl5vg3nng3zdso57aa7m
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