ConvNet frameworks for multi-modal fake news detection

Raj, Chahat; Meel, Priyanka

doi:10.1007/s10489-021-02345-y

Cited by 31 publications

(13 citation statements)

References 25 publications

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“…The proposed system is based on LSTM with CNN, which effectively classify tweets into rumors and non-rumors. Chahat Raj et al [34] built textual and visual modules to aid the research over fake news detection. They proposed a multi-modal Coupled ConvNet architecture.…”

Section: Classical Deep Learning Methodsmentioning

confidence: 99%

Rumor detection on social media using hierarchically aggregated feature via graph neural networks

Dong

et al. 2022

Appl Intell

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In the era of the Internet and big data, online social media platforms have been developing rapidly, which accelerate rumors circulation. Rumor detection on social media is a worldwide challenging task due to rumor’s feature of high speed, fragmental information and extensive range. Most existing approaches identify rumors based on single-layered hybrid features like word features, sentiment features and user characteristics, or multimodal features like the combination of text features and image features. Some researchers adopted the hierarchical structure, but they neither used rumor propagation nor made full use of its retweet posts. In this paper, we propose a novel model for rumor detection based on Graph Neural Networks (GNN), named Hierarchically Aggregated Graph Neural Networks (HAGNN). This task focuses on capturing different granularities of high-level representations of text content and fusing the rumor propagation structure. It applies a Graph Convolutional Network (GCN) with a graph of rumor propagation to learn the text-granularity representations with the spreading of events. A GNN model with a document graph is employed to update aggregated features of both word and text granularity, it helps to form final representations of events to detect rumors. Experiments on two real-world datasets demonstrate the superiority of the proposed method over the baseline methods. Our model achieves the accuracy of 95.7% and 88.2% on the Weibo dataset Ma et al. 2017 and the CED dataset Song et al. IEEE Trans Knowl Data Eng 33(8):3035–3047, 2019respectively.

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Section: Classical Deep Learning Methodsmentioning

confidence: 99%

Rumor detection on social media using hierarchically aggregated feature via graph neural networks

Dong

et al. 2022

Appl Intell

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“…Similar models are proposed by Dutta and Chakraborty ( 2020 ) and Xu et al ( 2020 ), wherein darknet and social media platforms are analyzed for fake news using deep learning methods. Similarly, Raj and Meel ( 2021 ) suggested a multi-modal fake news detection model that included Text-CNN and Image-CNN modules on two separate text datasets, wherein their Text-CNN module performs averaged 94.91% accuracy. The authors Shim et al ( 2021 ) suggested using the composition structure of web links containing news content as a new source of information for detecting fake news.…”

Section: Literature Reviewmentioning

confidence: 99%

A cooperative deep learning model for fake news detection in online social networks

Mallick

Mishra

Senapati

2023

J Ambient Intell Human Comput

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“…Freire et al [51] used crowd signals inspired by the meta information for detecting fake news. Raj and Meel [52] also utilized covNet for text and images but used the early fusion technique. Similarly, Wang et al [53] also employed CNN with an attention mechanism.…”

Section: Multi-modal Approachmentioning

confidence: 99%

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

2023

KSII TIIS

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Social media play a significant role in communicating information across the globe, connecting with loved ones, getting the news, communicating ideas, etc. However, a group of people uses social media to spread fake information, which has a bad impact on society. Therefore, minimizing fake news and its detection are the two primary challenges that need to be addressed. This paper presents a multi-modal deep learning technique to address the above challenges. The proposed modal can use and process visual and textual features. Therefore, it has the ability to detect fake information from visual and textual data. We used EfficientNet-B0 and a sentence transformer, respectively, for detecting counterfeit images and for textural learning. Feature embedding is performed at individual channels, whilst fusion is done at the last classification layer. The late fusion is applied intentionally to mitigate the noisy data that are generated by multi-modalities. Extensive experiments are conducted, and performance is evaluated against state-of-the-art methods. Three real-world benchmark datasets, such as MediaEval (Twitter), Weibo, and Fakeddit, are used for experimentation. Result reveals that the proposed modal outperformed the state-of-the-art methods and achieved an accuracy of 86.48%, 82.50%, and 88.80%, respectively, for MediaEval (Twitter), Weibo, and Fakeddit datasets.

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ConvNet frameworks for multi-modal fake news detection

Cited by 31 publications

References 25 publications

Rumor detection on social media using hierarchically aggregated feature via graph neural networks

Rumor detection on social media using hierarchically aggregated feature via graph neural networks

A cooperative deep learning model for fake news detection in online social networks

FakedBits- Detecting Fake Information on Social Platforms using Multi-Modal Features

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