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 create a random sample of Twitter users in the US: 1% Stream, Bounding Box, Location Query, and Language Query. Then, we compare these methods according to their tweet- and user-level metrics as well as their accuracy in estimating the US population. Our results show that users collected by the 1% Stream method tend to have more tweets, tweets per day, followers, and frie

SUBMITTER: Alizadeh M 

PROVIDER: S-EPMC11861139 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature