  This paper describes our participation in Task 3 and Task 5 of the #SMM4H
(Social Media Mining for Health) 2024 Workshop, explicitly targeting the
classification challenges within tweet data. Task 3 is a multi-class
classification task centered on tweets discussing the impact of outdoor
environments on symptoms of social anxiety. Task 5 involves a binary
classification task focusing on tweets reporting medical disorders in children.
We applied transfer learning from pre-trained encoder-decoder models such as
BART-base and T5-small to identify the labels of a set of given tweets. We also
presented some data augmentation methods to see their impact on the model
performance. Finally, the systems obtained the best F1 score of 0.627 in Task 3
and the best F1 score of 0.841 in Task 5.
