SpotFake+: A Multimodal Framework for Fake News Detection via Transfer Learning (Student Abstract)

Authors

  • Shivangi Singhal Indraprastha Institute of Information Technology, Delhi
  • Anubha Kabra Delhi Technological University
  • Mohit Sharma Indraprastha Institute of Information Technology, Delhi
  • Rajiv Ratn Shah Indraprastha Institute of Information Technology, Delhi
  • Tanmoy Chakraborty Indraprastha Institute of Information Technology, Delhi
  • Ponnurangam Kumaraguru Indraprastha Institute of Information Technology, Delhi

DOI:

https://doi.org/10.1609/aaai.v34i10.7230

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.

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Published

2020-04-03

How to Cite

Singhal, S., Kabra, A., Sharma, M., Shah, R. R., Chakraborty, T., & Kumaraguru, P. (2020). SpotFake+: A Multimodal Framework for Fake News Detection via Transfer Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13915-13916. https://doi.org/10.1609/aaai.v34i10.7230

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Section

Student Abstract Track