Fake news detection is the task of detecting forms of news consisting of deliberate disinformation or hoaxes spread via traditional news media (print and broadcast) or online social media (Source: Adapted from Wikipedia).
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One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing.
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.
Ranked #1 on Fake News Detection on Grover-Mega
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.
In this paper, we present liar: a new, publicly available dataset 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 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.
As a contribution towards this objective, we show that Facebook posts can be classified with high accuracy as hoaxes or non-hoaxes on the basis of the users who "liked" them.