  Automatically identifying fake news from the Internet is a challenging
problem in deception detection tasks. Online news is modified constantly during
its propagation, e.g., malicious users distort the original truth and make up
fake news. However, the continuous evolution process would generate
unprecedented fake news and cheat the original model. We present the Fake News
Evolution (FNE) dataset: a new dataset tracking the fake news evolution
process. Our dataset is composed of 950 paired data, each of which consists of
articles representing the three significant phases of the evolution process,
which are the truth, the fake news, and the evolved fake news. We observe the
features during the evolution and they are the disinformation techniques, text
similarity, top 10 keywords, classification accuracy, parts of speech, and
sentiment properties.
