DeepNet: An Efficient Neural Network for Fake News Detection using News-User Engagements

Kaliyar, Rohit Kumar; Kumar, Pawan; Kumar, M. Anand; Narkhede, Meenal; Namboodiri, Sreyas; Mishra, Sneha

doi:10.1109/icccs49678.2020.9277353

Cited by 15 publications

(2 citation statements)

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“…The researchers found that the accuracy of the model results based on BERT and ResNet in using features of both text and images was better than the model that uses only text in detecting fake news. Kaliyar, Kumar [ 77 ] suggested a detection model for fake news that relies on DeepNet by using the real data of BuzzFeed and Fakeddit datasets. By utilizing tensor factorization, which integrates news content and social context-based data, DeepNet outperformed current fake news detection models.…”

Section: Previous Studiesmentioning

confidence: 99%

Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users’ Comments

Hamed

Aziz

Yaakub

2023

Sensors

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Nowadays, social media has become the main source of news around the world. The spread of fake news on social networks has become a serious global issue, damaging many aspects, such as political, economic, and social aspects, and negatively affecting the lives of citizens. Fake news often carries negative sentiments, and the public’s response to it carries the emotions of surprise, fear, and disgust. In this article, we extracted features based on sentiment analysis of news articles and emotion analysis of users’ comments regarding this news. These features were fed, along with the content feature of the news, to the proposed bidirectional long short-term memory model to detect fake news. We used the standard Fakeddit dataset that contains news titles and comments posted regarding them to train and test the proposed model. The suggested model, using extracted features, provided a high detection accuracy of 96.77% of the Area under the ROC Curve measure, which is higher than what other state-of-the-art studies offer. The results prove that the features extracted based on sentiment analysis of news, which represents the publisher’s stance, and emotion analysis of comments, which represent the crowd’s stance, contribute to raising the efficiency of the detection model.

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Section: Previous Studiesmentioning

confidence: 99%

Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users’ Comments

Hamed

Aziz

Yaakub

2023

Sensors

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“…Apart from the previous studies, several authors have proposed fake news classification models and have evaluated them using the Fakeddit dataset. Kaliyar et al [53] propose the DeepNet model for the binary classification of fake news. This model is made up of one embedding layer, three convolutional layers, one LSTM layer, seven dense layers, ReLU for activation, and, finally, the softmax function for the binary classification.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Fake News Detection

Segura-Bedmar

Alonso-Bartolome

2022

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Over the last few years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have on different segments of our society. Thus, the development of tools for the automatic detection of fake news plays an important role in the prevention of its negative effects. Most attempts to detect and classify false content focus only on using textual information. Multimodal approaches are less frequent and they typically classify news either as true or fake. In this work, we perform a fine-grained classification of fake news on the Fakeddit dataset, using both unimodal and multimodal approaches. Our experiments show that the multimodal approach based on a Convolutional Neural Network (CNN) architecture combining text and image data achieves the best results, with an accuracy of 87%. Some fake news categories, such as Manipulated content, Satire, or False connection, strongly benefit from the use of images. Using images also improves the results of the other categories but with less impact. Regarding the unimodal approaches using only text, Bidirectional Encoder Representations from Transformers (BERT) is the best model, with an accuracy of 78%. Exploiting both text and image data significantly improves the performance of fake news detection.

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