Comparing Methods for Creating a National Random Sample of Twitter Users

Meysam Alizadeh; Darya Zare; Zeynab Samei; Mohammadamin Alizadeh; Mael Kubli; Mohammadhadi Aliahmadi; Sarvenaz Ebrahimi; Fabrizio Gilardi

arXiv · 2402.04879

Comparing Methods for Creating a National Random Sample of Twitter Users

Meysam Alizadeh·Darya Zare·Zeynab Samei·Mohammadamin Alizadeh·Mael Kubli·Mohammadhadi Aliahmadi·Sarvenaz Ebrahimi·Fabrizio Gilardi

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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.

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Meysam Alizadeh, Darya Zare, Zeynab Samei, Mohammadamin Alizadeh, Mael Kubli, Mohammadhadi Aliahmadi, Sarvenaz Ebrahimi, Fabrizio Gilardi. 2024-02-07. Comparing Methods for Creating a National Random Sample of Twitter Users. https://arxiv.org/abs/2402.04879

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