How does Truth Evolve into Fake News? An Empirical Study of Fake News Evolution
This work addresses the challenge of deception detection for online news platforms by providing a dataset to study fake news evolution, though it is incremental as it focuses on data collection rather than a new method.
The authors tackled the problem of identifying fake news by creating the Fake News Evolution (FNE) dataset, which tracks how truth evolves into fake news through 950 paired articles across three phases, and they observed features like disinformation techniques and text similarity.
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.