Representing the Twittersphere: Archiving a representative sample of Twitter data under resource constraints

Airo Hino and Robert A. Fahey

International Journal of Information Management, 2019, vol. 48, issue C, 175-184

Abstract: The rising popularity of social media posts, most notably Twitter posts, as a data source for social science research poses significant problems with regard to access to representative, high-quality data for analysis. Cheap, publicly available data such as that obtained from Twitter's public application programming interfaces is often of low quality, while high-quality data is expensive both financially and computationally. Moreover, data is often available only in real-time, making post-hoc analysis difficult or impossible. We propose and test a methodology for inexpensively creating an archive of Twitter data through population sampling, yielding a database that is highly representative of the targeted user population (in this test case, the entire population of Japanese-language Twitter users). Comparing the tweet volume, keywords, and topics found in our sample data set with the ground truth of Twitter's full data feed confirmed a very high degree of representativeness in the sample. We conclude that this approach yields a data set that is suitable for a wide range of post-hoc analyses, while remaining cost effective and accessible to a wide range of researchers.

Keywords: Twitter; Social media; Sampling; Representativeness; Data collection (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:48:y:2019:i:c:p:175-184

DOI: 10.1016/j.ijinfomgt.2019.01.019

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