Comparing Methods for Creating a National Random Sample of Twitter Users
Abstract
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.
Keywords
Cite
@article{arxiv.2402.04879,
title = {Comparing Methods for Creating a National Random Sample of Twitter Users},
author = {Meysam Alizadeh and Darya Zare and Zeynab Samei and Mohammadamin Alizadeh and Mael Kubli and Mohammadhadi Aliahmadi and Sarvenaz Ebrahimi and Fabrizio Gilardi},
journal= {arXiv preprint arXiv:2402.04879},
year = {2024}
}