  Fake news has altered society in negative ways in politics and culture. It
has adversely affected both online social network systems as well as offline
communities and conversations. Using automatic machine learning classification
models is an efficient way to combat the widespread dissemination of fake news.
However, a lack of effective, comprehensive datasets has been a problem for
fake news research and detection model development. Prior fake news datasets do
not provide multimodal text and image data, metadata, comment data, and
fine-grained fake news categorization at the scale and breadth of our dataset.
We present Fakeddit, a novel multimodal dataset consisting of over 1 million
samples from multiple categories of fake news. After being processed through
several stages of review, the samples are labeled according to 2-way, 3-way,
and 6-way classification categories through distant supervision. 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.
