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With social media datasets being increasingly shared by researchers, it alsopresents the caveat that those datasets are not always completely replicable.Having to adhere to requirements of platforms like Twitter, researchers cannotrelease the raw data and instead have to release a list of unique identifiers,which others can then use to recollect the data from the platform themselves.This leads to the problem that subsets of the data may no longer be available,as content can be deleted or user accounts deactivated. To quantify the impactof content deletion in the replicability of datasets in a long term, we performa longitudinal analysis of the persistence of 30 Twitter datasets, whichinclude over 147 million tweets. Having the original datasets collected between2012 and 2016, and recollecting them later by using the tweet IDs, we look atfour different factors that quantify the extent to which recollected datasetsresemble original ones: completeness, representativity, similarity andchangingness. Even though the ratio of available tweets keeps decreasing as thedataset gets older, we find that the textual content of the recollected subsetis still largely representative of the whole dataset that was originallycollected. The representativity of the metadata, however, keeps decreasing overtime, both because the dataset shrinks and because certain metadata, such asthe users' number of followers, keeps changing. Our study has importantimplications for researchers sharing and using publicly shared Twitter datasetsin their research.

A Longitudinal Assessment of the Persistence of Twitter Datasets