  Fake information poses one of the major threats for society in the 21st
century. Identifying misinformation has become a key challenge due to the
amount of fake news that is published daily. Yet, no approach is established
that addresses the dynamics and versatility of fake news editorials. Instead of
classifying content, we propose an evidence retrieval approach to handle fake
news. The learning task is formulated as an unsupervised machine learning
problem. For validation purpose, we provide the user with a set of news
articles from reliable news sources supporting the hypothesis of the news
article in query and the final decision is left to the user. Technically we
propose a two-step process: (i) Aggregation-step: With information extracted
from the given text we query for similar content from reliable news sources.
(ii) Refining-step: We narrow the supporting evidence down by measuring the
semantic distance of the text with the collection from step (i). The distance
is calculated based on Word2Vec and the Word Mover's Distance. In our
experiments, only content that is below a certain distance threshold is
considered as supporting evidence. We find that our approach is agnostic to
concept drifts, i.e. the machine learning task is independent of the hypotheses
in a text. This makes it highly adaptable in times where fake news is as
diverse as classical news is. Our pipeline offers the possibility for further
analysis in the future, such as investigating bias and differences in news
reporting.
