  This paper surveys and presents recent academic work carried out within the
field of stance classification and fake news detection. Echo chambers and the
model organism problem are examples that pose challenges to acquire data with
high quality, due to opinions being polarised in microblogs. Nevertheless it is
shown that several machine learning approaches achieve promising results in
classifying stance. Some use crowd stance for fake news detection, such as the
approach in [Dungs et al., 2018] using Hidden Markov Models. Furthermore
feature engineering have significant importance in several approaches, which is
shown in [Aker et al., 2017]. This paper additionally includes a proposal of a
system implementation based on the presented survey.

Computation and Language;Computers and Society;Social and Information Networks