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        <title>A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements</title>
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    <abstract>Fake news, rumor, incorrect information, and misinformation detection are nowadays crucial issues as these might have serious consequences for our social fabrics. Such information is increasing rapidly due to the availability of enormous web information sources including social media feeds, news blogs, online newspapers etc. In this paper, we develop various deep learning models for detecting fake news and classifying them into the pre-defined fine-grained categories. At first, we develop individual models based on Convolutional Neural Network (CNN), and Bi-directional Long Short Term Memory (Bi-LSTM) networks. The representations obtained from these two models are fed into a Multi-layer Perceptron Model (MLP) for the final classification. Our experiments on a benchmark dataset show promising results with an overall accuracy of 44.87%, which outperforms the current state of the arts.</abstract>
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%0 Conference Proceedings
%T A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements
%A Roy, Arjun
%A Basak, Kingshuk
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Bhattacharya, Pushpak
%S Proceedings of the 16th International Conference on Natural Language Processing
%D 2019
%8 December
%I NLP Association of India
%C International Institute of Information Technology, Hyderabad, India
%F roy-etal-2019-deep
%X Fake news, rumor, incorrect information, and misinformation detection are nowadays crucial issues as these might have serious consequences for our social fabrics. Such information is increasing rapidly due to the availability of enormous web information sources including social media feeds, news blogs, online newspapers etc. In this paper, we develop various deep learning models for detecting fake news and classifying them into the pre-defined fine-grained categories. At first, we develop individual models based on Convolutional Neural Network (CNN), and Bi-directional Long Short Term Memory (Bi-LSTM) networks. The representations obtained from these two models are fed into a Multi-layer Perceptron Model (MLP) for the final classification. Our experiments on a benchmark dataset show promising results with an overall accuracy of 44.87%, which outperforms the current state of the arts.
%U https://aclanthology.org/2019.icon-1.2
%P 9-17

Markdown (Informal)

[A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements](https://aclanthology.org/2019.icon-1.2) (Roy et al., ICON 2019)

• A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements (Roy et al., ICON 2019)

ACL

• Arjun Roy, Kingshuk Basak, Asif Ekbal, and Pushpak Bhattacharyya. 2019. A Deep Ensemble Framework for Fake News Detection and Multi-Class Classification of Short Political Statements. In Proceedings of the 16th International Conference on Natural Language Processing, pages 9–17, International Institute of Information Technology, Hyderabad, India. NLP Association of India.