Coping with low data availability for social media crisis message
  categorisation
Congcong Wang
During crisis situations, social media allows people to quickly share
information, including messages requesting help. This can be valuable to
emergency responders, who need to categorise and prioritise these messages
based on the type of assistance being requested. However, the high volume of
messages makes it difficult to filter and prioritise them without the use of
computational techniques. Fully supervised filtering techniques for crisis
message categorisation typically require a large amount of annotated training
data, but this can be difficult to obtain during an ongoing crisis and is
expensive in terms of time and labour to create.
  This thesis focuses on addressing the challenge of low data availability when
categorising crisis messages for emergency response. It first presents domain
adaptation as a solution for this problem, which involves learning a
categorisation model from annotated data from past crisis events (source
domain) and adapting it to categorise messages from an ongoing crisis event
(target domain). In many-to-many adaptation, where the model is trained on
multiple past events and adapted to multiple ongoing events, a multi-task
learning approach is proposed using pre-trained language models. This approach
outperforms baselines and an ensemble approach further improves performance...