Comparing Methods for Creating a National Random Sample of Twitter Users
<|reference_start|>Comparing Methods for Creating a National Random Sample of Twitter Users: Twitter data has been widely used by researchers across various social and computer science disciplines. A common aim when working with Twitter data is the construction of a random sample of users from a given country. However, while several methods have been proposed in the literature, their comparative performance is mostly unexplored. In this paper, we implement four common methods to collect a random sample of Twitter users in the US: 1% Stream, Bounding Box, Location Query, and Language Query. Then, we compare the methods according to their tweet- and user-level metrics as well as their accuracy in estimating US population with and without using inclusion probabilities of various demographics. Our results show that the 1% Stream method performs differently than others in tweet- and user-level metrics, and best for the construction of a population representative sample. We discuss the conditions under which the 1% Stream method may not be suitable and suggest the Bounding Box method as the second-best method to use.<|reference_end|>
@article{alizadeh2024comparing,
    title={Comparing Methods for Creating a National Random Sample of Twitter Users},
    author={Meysam Alizadeh, Darya Zare, Zeynab Samei, Mohammadamin Alizadeh, Mael
  Kubli, Mohammadhadi Aliahmadi, Sarvenaz Ebrahimi, Fabrizio Gilardi},
    journal={arXiv preprint arXiv:2402.04879},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04879},
    primaryClass={cs.SI}
}