First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination.
One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events.
Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume.
Incorporating hierarchical discourse-level structure of fake and real news articles is one crucial step toward a better understanding of how these articles are structured.
In this paper, we present liar: a new, publicly available dataset for fake news detection.
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.