  Stance detection in fake news is an important component in news veracity
assessment because this process helps fact-checking by understanding stance to
a central claim from different information sources. The Fake News Challenge
Stage 1 (FNC-1) held in 2017 was setup for this purpose, which involves
estimating the stance of a news article body relative to a given headline. This
thesis starts from the error analysis for the three top-performing systems in
FNC-1. Based on the analysis, a simple but tough-to-beat Multilayer Perceptron
system is chosen as the baseline. Afterwards, three approaches are explored to
improve baseline.The first approach explores the possibility of improving the
prediction accuracy by adding extra keywords features when training a model,
where keywords are converted to an indicator vector and then concatenated to
the baseline features. A list of keywords is manually selected based on the
error analysis, which may best reflect some characteristics of fake news titles
and bodies. To make this selection process automatically, three algorithms are
created based on Mutual Information (MI) theory: keywords generator based on MI
stance class, MI customised class, and Pointwise MI algorithm. The second
approach is based on word embedding, where word2vec model is introduced and two
document similarities calculation algorithms are implemented: wor2vec cosine
similarity and WMD distance. The third approach is ensemble learning. Different
models are configured together with two continuous outputs combining
algorithms. The 10-fold cross validation reveals that the ensemble of three
neural network models trained from simple bag-of-words features gives the best
performance. It is therefore selected to compete in FNC-1. After
hyperparameters fine tuning, the selected deep ensemble model beats the FNC-1
winner team by a remarkable 34.25 marks under FNC-1's evaluation metric.
