  Given the rapidly evolving nature of social media and people's views, word
usage changes over time. Consequently, the performance of a classifier trained
on old textual data can drop dramatically when tested on newer data. While
research in stance classification has advanced in recent years, no effort has
been invested in making these classifiers have persistent performance over
time. To study this phenomenon we introduce two novel large-scale, longitudinal
stance datasets. We then evaluate the performance persistence of stance
classifiers over time and demonstrate how it decays as the temporal gap between
training and testing data increases. We propose a novel approach to mitigate
this performance drop, which is based on temporal adaptation of the word
embeddings used for training the stance classifier. This enables us to make use
of readily available unlabelled data from the current time period instead of
expensive annotation efforts. We propose and compare several approaches to
embedding adaptation and find that the Incremental Temporal Alignment (ITA)
model leads to the best results in reducing performance drop over time.
