Convolutional neural network with margin loss for fake news detection

Goldani, Mohammad Hadi; Safabakhsh, Reza; Momtazi, Saeedeh

doi:10.1016/j.ipm.2020.102418

Cited by 92 publications

(48 citation statements)

References 53 publications

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“…Fig. 5 describes the comparative study on accuracy metric, and from the figure, it has been found that author [16] has achieved nearly 99.9% accuracy on the Liar dataset, which is highest in terms of both similar and non-similar datasets [20] [21]. The author has implemented a simple static word embedding technique to extract the features.…”

Section: Resultsmentioning

confidence: 99%

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An Empirical Study on Fake News Detection System using Deep and Machine Learning Ensemble Techniques

Divya¹,

Banik²

2021

IJACSA

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With the revolution that happened in electronic gadgets in the past few years, information sharing has evolved into a new era that can spread the news globally in a fraction of minutes, either through yellow media or through satellite communication without any proper authentication. At the same time, all of us are aware that with the increase of different social media platforms, many organizations try to grab people's attention by creating fake news about celebrities, politicians (or) politics, branded products, and others. There are three ways to generate fake news: tampering with an image using advanced morphing tools; this is generally a popular technique while posting phony information about the celebrities (or) cybercrimes related to women. The second one deals with the reposting of the old happenings with new fake content injected into it. For example, in generally few social media platforms either to increase their TRP ratings or to expand their subscribers, they create old news that happened somewhere years ago as latest one with new fake content like by changing the date, time, locations, and other important information and tries to make them viral across the globe. The third one deals with the image/video real happened at an event or place, but media try to change the content with a false claim instead of the original one that occurred. A few decades back, researchers started working on fake news detection topics with the help of textual data. In the recent era, few researchers worked on images and text data using traditional and ensemble deep and machine learning algorithms, but they either suffer from overfitting problems due to insufficient data or unable to extract the complex semantic relations between documents. The proposed system designs a transfer learning environment where Neural Style Transfer Learning takes care of the size and quality of the datasets. It also enhances the auto-encoders by customizing the hidden layers to handle complex problems in the real world.

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Section: Resultsmentioning

confidence: 99%

“…In [16], Mohammad HadiGoldani et al incrementally designed a margin loss CNN. This research aims to create a less error-prone function that reduces the cross-entropy generated by the softmax activation function.…”

Section: Literature Surveymentioning

confidence: 99%

An Empirical Study on Fake News Detection System using Deep and Machine Learning Ensemble Techniques

Divya¹,

Banik²

2021

IJACSA

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“…It is pretty simple because every input only has a 2-dimensional matrix of tokens, and the output is also a 2-dimensional matrix having a smaller size than the input. In the fake news detection task, several studies experimented with CNN: Goldani et al [45] The proposed solution turns out to be much faster than the conventional approach and ensures increased accuracy. The method makes the system particularly suitable in scenarios where large databases of images are analyzed, like over social networks.…”

Section: Fake News With Cnn-basedmentioning

confidence: 99%

Transfer learning and GRU-CRF augmentation for Covid-19 fake news detection

Karnyoto

Sun

Liu

et al. 2022

ComSIS

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The spread of fake news on online media is very dangerous and can lead to casualties, effects on psychology, character assassination, elections for political parties, and state chaos. Fake news that concerning Covid-19 massively spread during the pandemic. Detecting misinformation on the Internet is an essential and challenging task since humans face difficulty detecting fake news. We applied BERT and GPT2 as pre-trained using the BiGRU-Att-CapsuleNet model and BiGRU-CRF features augmentation to solve Fake News detection in Constraint @ AAAI2021 - COVID19 Fake News Detection in English Dataset. This research proved that our hybrid model with augmentation got better accuracy compared to our baseline model. It also showed that BERT gave a better result than GPT2 in all models; the highest accuracy we achieved for BERT is 0.9196, and GPT2 is 0.8986.

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“…Many studies related to CNN have shown excellent performance on unstructured data such as images, video, voice, and audio [16]. Moreover, in many studies using text data, models combining CNN have been developed and have demonstrated suitable performance [17][18][19][20][21]. The proposed model incorporates a CNN model with high-dimensional business tabular data; the related works are described briefly as follows.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network

Na²,

Park

et al. 2021

Applied Sciences

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Outbound telemarketing is an efficient direct marketing method wherein telemarketers solicit potential customers by phone to purchase or subscribe to products or services. However, those who are not interested in the information or offers provided by outbound telemarketing generally experience such interactions negatively because they perceive telemarketing as spam. In this study, therefore, we investigate the use of deep learning models to predict the success of outbound telemarketing for insurance policy loans. We propose an explainable multiple-filter convolutional neural network model called XmCNN that can alleviate overfitting and extract various high-level features using hundreds of input variables. To enable the practical application of the proposed method, we also examine ensemble models to further improve its performance. We experimentally demonstrate that the proposed XmCNN significantly outperformed conventional deep neural network models and machine learning models. Furthermore, a deep learning ensemble model constructed using the XmCNN architecture achieved the lowest false positive rate (4.92%) and the highest F1-score (87.47%). We identified important variables influencing insurance policy loan prediction through the proposed model, suggesting that these factors should be considered in practice. The proposed method may increase the efficiency of outbound telemarketing and reduce the spam problems caused by calling non-potential customers.

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Convolutional neural network with margin loss for fake news detection

Cited by 92 publications

References 53 publications

An Empirical Study on Fake News Detection System using Deep and Machine Learning Ensemble Techniques

An Empirical Study on Fake News Detection System using Deep and Machine Learning Ensemble Techniques

Transfer learning and GRU-CRF augmentation for Covid-19 fake news detection

Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network

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