Tree LSTMs with Convolution Units to Predict Stance and Rumor
Veracity in Social Media Conversations
Abstract
Learning from social-media conversations has
gained significant attention recently because
of its applications in areas like rumor detection. In this research, we propose a new way
to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies
effectively. Moreover, we propose to use convolution units in Tree LSTMs that are better
at learning patterns in features obtained from
the source and reply posts. Our Tree LSTM
models employ multi-task (stance + rumor)
learning and propagate the useful stance signal up in the tree for rumor classification at the
root node. The proposed models achieve stateof-the-art performance, outperforming the current best model by 12% and 15% on F1-macro
for rumor-veracity classification and stance
classification tasks respectively