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NewsBag: A multimodal benchmark dataset for fake news detection
S. Jindal, R. Sood, , , T. Chakraborty
Published in CEUR-WS
Volume: 2560
Pages: 138 - 145
The spread of fake news poses a critical problem in today's world, where most individuals consume information from online platforms. Fake news detection is an arduous task, marred by the lack of a robust ground truth database for training classification models. Fake news articles manipulate multimedia content (text and images) to disseminate false information. Existing fake news datasets are either small in size or predominantly contain unimodal data. We propose two novel benchmark multimodal datasets, consisting of text and images, to enhance the quality of fake news detection. The first dataset includes manually collected real and fake news data from multiple online sources. In the second dataset, we study the effect of data augmentation by using a Bag of Words approach to increase the quantity of fake news data. These datasets are significantly larger in size in comparison to the existing datasets. We conducted extensive experiments by training state of the art unimodal and multimodal fake news detection algorithms on our dataset and comparing it with the results on existing datasets, showing the effectiveness of our proposed datasets. The experimental results show that data augmentation to increase the quantity of fake news does not hamper the accuracy of fake news detection. The results also conclude that the utilization of multimodal data for fake news detection substantially outperforms the unimodal algorithms. © 2020 for this paper by its authors.
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JournalCEUR Workshop Proceedings