  Fake news may be intentionally created to promote economic, political and
social interests, and can lead to negative impacts on humans beliefs and
decisions. Hence, detection of fake news is an emerging problem that has become
extremely prevalent during the last few years. Most existing works on this
topic focus on manual feature extraction and supervised classification models
leveraging a large number of labeled (fake or real) articles. In contrast, we
focus on content-based detection of fake news articles, while assuming that we
have a small amount of labels, made available by manual fact-checkers or
automated sources. We argue this is a more realistic setting in the presence of
massive amounts of content, most of which cannot be easily factchecked. To that
end, we represent collections of news articles as multi-dimensional tensors,
leverage tensor decomposition to derive concise article embeddings that capture
spatial/contextual information about each news article, and use those
embeddings to create an article-by-article graph on which we propagate limited
labels. Results on three real-world datasets show that our method performs on
par or better than existing models that are fully supervised, in that we
achieve better detection accuracy using fewer labels. In particular, our
proposed method achieves 75.43% of accuracy using only 30% of labels of a
public dataset while an SVM-based classifier achieved 67.43%. Furthermore, our
method achieves 70.92% of accuracy in a large dataset using only 2% of labels.
