Knowledge augmented transformer for adversarial multidomain multiclassification multimodal fake news detection

Song, Chenguang; Ning, Nianwen; Zhang, Yunlei; Wu, Bin

doi:10.1016/j.neucom.2021.07.077

Cited by 23 publications

(13 citation statements)

References 26 publications

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“…Several survey and review papers that contain detection challenges and potential open issues have been published recently, including but not limited to [34][35][36][37][40][41][42][43]. Many issues have been researched related to feature extraction [6][7][8][9][10], representation [8,[11][12][13][14][15][16][17], classification [6,12,[18][19][20][21][22][23][24][25], and model design [10,16,21,22,[26][27][28][29][30][31][32]. Various solutions have been investigated using statical, traditional machine and deep learning and natural languageprocessing techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Several approaches have been suggested to identify fake news. Numerous issues have been studied related to feature extraction [6][7][8][9][10], representation [8,[11][12][13][14][15][16][17], classification [6,12,[18][19][20][21][22][23][24][25], and model design [10,16,21,22,[26][27][28][29][30][31][32]. Based on the representative features that are employed, fake news detection techniques can be can be categorized into four groups: content-, knowledge-, users-, and propagation-based features.…”

Section: Introductionmentioning

confidence: 99%

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Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder

et al. 2023

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Today, fake news is a growing concern due to its devastating impacts on communities. The rise of social media, which many users consider the main source of news, has exacerbated this issue because individuals can easily disseminate fake news more quickly and inexpensive with fewer checks and filters than traditional news media. Numerous approaches have been explored to automate the detection and prevent the spread of fake news. However, achieving accurate detection requires addressing two crucial aspects: obtaining the representative features of effective news and designing an appropriate model. Most of the existing solutions rely solely on content-based features that are insufficient and overlapping. Moreover, most of the models used for classification are constructed with the concept of a dense features vector unsuitable for short news sentences. To address this problem, this study proposed a Web-Informed-Augmented Fake News Detection Model using Stacked Layers of Convolutional Neural Network and Deep Autoencoder called ICNN-AEN-DM. The augmented information is gathered from web searches from trusted sources to either support or reject the claims in the news content. Then staked layers of CNN with a deep autoencoder were constructed to train a probabilistic deep learning-base classifier. The probabilistic outputs of the stacked layers were used to train decision-making by staking multilayer perceptron (MLP) layers to the probabilistic deep learning layers. The results based on extensive experiments challenging datasets show that the proposed model performs better than the related work models. It achieves 26.6% and 8% improvement in detection accuracy and overall detection performance, respectively. Such achievements are promising for reducing the negative impacts of fake news on communities.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder

et al. 2023

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“…Della Vedova et al ( 2018) proposed a method that has been implemented in a chatbot Facebook Messenger and validated with a realistic application that has an accuracy of 81.7 percent in detecting fake news. Similarly, A temporal evolving graph neural network for false news detection has been suggested by Song et al (2021a;2021b), and it has been demonstrated that the proposed model outperforms existing fake news detection approaches. A study by Saadany et al (2020) was published in which the authors recommended utilising machine learning models to classify the linguistic aspects of Arabic Satirical Fake News in order to determine whether or not the Arabic news is true.…”

Section: Related Workmentioning

confidence: 99%

Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News

Aljwari

Alkaberi

Alshutayri

et al. 2022

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There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that were made up were used. They are found by using Term Frequency-Inverse Document Frequency (TF-IDF). The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TF-IDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news.

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“…The fake profile people purposely design content to gather attention of other users. Due to the social network's qualities of quick distribution and low cost, a significant amount of news content is disseminated there quickly [1].…”

Section: Introductionmentioning

confidence: 99%

Fake Accounts Identification in Mobile Communication Networks Based on Machine Learning

Hassan

Alhalangy²,

Alzahrani³

2023

Int. J. Interact. Mob. Technol.

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Fake accounts on online social networks are increasing today with an increase in the number of active social network users. Social media websites allow users to share thoughts, facts, views and re-sharing these into various networks. Social media platforms provide users with enormous valuable information but with this great amount of information in social media, many issues like fake profile, online hacking have also grown. The fake profiles in online social sites create fake news and share unwanted material which contains spam links that affect natural users. The massive issue in social media communication networks is spam and it is necessary to identify fake profiles to stop spam. In this paper, a supervised machine learning algorithm called support vector machine (SVM) is used to identify fake accounts on social media effectively. In order to automatically identify fake online profiles, Random Forest classifier is used with SVM. With this concept, it can be applied online easily to identify millions of accounts that cannot be examined manually. The result of this model is compared with other identification techniques and the results show that the proposed algorithm performs better with high precision and recall. This method efficiently safeguards social media networks from online threats and attacks.

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Knowledge augmented transformer for adversarial multidomain multiclassification multimodal fake news detection

Cited by 23 publications

References 26 publications

Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder

Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder

Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News

Fake Accounts Identification in Mobile Communication Networks Based on Machine Learning

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