  The past decade has seen a substantial rise in the amount of mis- and
disinformation online, from targeted disinformation campaigns to influence
politics, to the unintentional spreading of misinformation about public health.
This development has spurred research in the area of automatic fact checking,
from approaches to detect check-worthy claims and determining the stance of
tweets towards claims, to methods to determine the veracity of claims given
evidence documents. These automatic methods are often content-based, using
natural language processing methods, which in turn utilise deep neural networks
to learn higher-order features from text in order to make predictions. As deep
neural networks are black-box models, their inner workings cannot be easily
explained. At the same time, it is desirable to explain how they arrive at
certain decisions, especially if they are to be used for decision making. While
this has been known for some time, the issues this raises have been exacerbated
by models increasing in size, and by EU legislation requiring models to be used
for decision making to provide explanations, and, very recently, by legislation
requiring online platforms operating in the EU to provide transparent reporting
on their services. Despite this, current solutions for explainability are still
lacking in the area of fact checking. This thesis presents my research on
automatic fact checking, including claim check-worthiness detection, stance
detection and veracity prediction. Its contributions go beyond fact checking,
with the thesis proposing more general machine learning solutions for natural
language processing in the area of learning with limited labelled data.
Finally, the thesis presents some first solutions for explainable fact
checking.
