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Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public.
Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim and has become a key component in applications like fake news detection, claim validation, and argument search.
In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i. e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection.
We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit.
In this paper, we show that Transfer Learning (TL) can be used to train robust fake news classifiers from little data, achieving 91% accuracy on a fake news dataset in the low-resourced Filipino language, reducing the error by 14% compared to established few-shot baselines.
In fighting against fake news, many fact-checking systems comprised of human-based fact-checking sites (e. g., snopes. com and politifact. com) and automatic detection systems have been developed in recent years.
We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data.
SOTA for Fake News Detection on Grover-Mega
The proliferation of fake news and its propagation on social media have become a major concern due to its ability to create devastating impacts.
Over the past few years, we have been witnessing the rise of misinformation on the Web.