SpotFake+: A Multimodal Framework for Fake News Detection via Transfer Learning (Student Abstract)
Abstract
In recent years, there has been a substantial rise in the consumption of news via online platforms. The ease of publication and lack of editorial rigour in some of these platforms have further led to the proliferation of fake news. In this paper, we study the problem of detecting fake news on the FakeNewsNet repository, a collection of full length articles along with associated images. We present SpotFake+, a multimodal approach that leverages transfer learning to capture semantic and contextual information from the news articles and its associated images and achieves the better accuracy for fake news detection. To the best of our knowledge, this is the first work that performs a multimodal approach for fake news detection on a dataset that consists of full length articles. It outperforms the performance shown by both single modality and multiple-modality models. We also release the pretrained model for the benefit of the community.
Cite
Text
Singhal et al. "SpotFake+: A Multimodal Framework for Fake News Detection via Transfer Learning (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7230Markdown
[Singhal et al. "SpotFake+: A Multimodal Framework for Fake News Detection via Transfer Learning (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/singhal2020aaai-spotfake/) doi:10.1609/AAAI.V34I10.7230BibTeX
@inproceedings{singhal2020aaai-spotfake,
title = {{SpotFake+: A Multimodal Framework for Fake News Detection via Transfer Learning (Student Abstract)}},
author = {Singhal, Shivangi and Kabra, Anubha and Sharma, Mohit and Shah, Rajiv Ratn and Chakraborty, Tanmoy and Kumaraguru, Ponnurangam},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {13915-13916},
doi = {10.1609/AAAI.V34I10.7230},
url = {https://mlanthology.org/aaai/2020/singhal2020aaai-spotfake/}
}