  Language evolves over time, and word meaning changes accordingly. This is
especially true in social media, since its dynamic nature leads to faster
semantic shifts, making it challenging for NLP models to deal with new content
and trends. However, the number of datasets and models that specifically
address the dynamic nature of these social platforms is scarce. To bridge this
gap, we present TempoWiC, a new benchmark especially aimed at accelerating
research in social media-based meaning shift. Our results show that TempoWiC is
a challenging benchmark, even for recently-released language models specialized
in social media.
