  With the proliferation of social media, many studies resort to social media
to construct datasets for developing social meaning understanding systems. For
the popular case of Twitter, most researchers distribute tweet IDs without the
actual text contents due to the data distribution policy of the platform. One
issue is that the posts become increasingly inaccessible over time, which leads
to unfair comparisons and a temporal bias in social media research. To
alleviate this challenge of data decay, we leverage a paraphrase model to
propose a new persistent English Twitter dataset for social meaning (PTSM).
PTSM consists of $17$ social meaning datasets in $10$ categories of tasks. We
experiment with two SOTA pre-trained language models and show that our PTSM can
substitute the actual tweets with paraphrases with marginal performance loss.
