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Sampling Twitter users for social science research: evidence from a systematic review of the literature

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  • Paula Vicente

    (ISCTE-Instituto Universitário de Lisboa)

Abstract

All social media platforms can be used to conduct social science research, but Twitter is the most popular as it provides its data via several Application Programming Interfaces, which allows qualitative and quantitative research to be conducted with its members. As Twitter is a huge universe, both in number of users and amount of data, sampling is generally required when using it for research purposes. Researchers only recently began to question whether tweet-level sampling—in which the tweet is the sampling unit—should be replaced by user-level sampling—in which the user is the sampling unit. The major rationale for this shift is that tweet-level sampling does not consider the fact that some core discussants on Twitter are much more active tweeters than other less active users, thus causing a sample biased towards the more active users. The knowledge on how to select representative samples of users in the Twitterverse is still insufficient despite its relevance for reliable and valid research outcomes. This paper contributes to this topic by presenting a systematic quantitative literature review of sampling plans designed and executed in the context of social science research in Twitter, including: (1) the definition of the target populations, (2) the sampling frames used to support sample selection, (3) the sampling methods used to obtain samples of Twitter users, (4) how data is collected from Twitter users, (5) the size of the samples, and (6) how research validity is addressed. This review can be a methodological guide for professionals and academics who want to conduct social science research involving Twitter users and the Twitterverse.

Suggested Citation

  • Paula Vicente, 2023. "Sampling Twitter users for social science research: evidence from a systematic review of the literature," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(6), pages 5449-5489, December.
    • Handle: RePEc:spr:qualqt:v:57:y:2023:i:6:d:10.1007_s11135-023-01615-w
    DOI: 10.1007/s11135-023-01615-w
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    References listed on IDEAS

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    1. Yu, Houqiang & Xiao, Tingting & Xu, Shenmeng & Wang, Yuefen, 2019. "Who posts scientific tweets? An investigation into the productivity, locations, and identities of scientific tweeters," Journal of Informetrics, Elsevier, vol. 13(3), pages 841-855.
    2. Darja Reuschke & Jed Long & Nick Bennett, 2021. "Locating creativity in the city using Twitter data," Environment and Planning B, , vol. 48(9), pages 2607-2622, November.
    3. Rachel Sharples, 2021. "Disrupting State Spaces: Asylum Seekers in Australia’s Offshore Detention Centres," Social Sciences, MDPI, vol. 10(3), pages 1-16, March.
    4. Kristen Olson, 2013. "Paradata for Nonresponse Adjustment," The ANNALS of the American Academy of Political and Social Science, , vol. 645(1), pages 142-170, January.
    5. Tomu Tominaga & Yoshinori Hijikata & Joseph A. Konstan, 2018. "How self-disclosure in Twitter profiles relate to anonymity consciousness and usage objectives: a cross-cultural study," Journal of Computational Social Science, Springer, vol. 1(2), pages 391-435, September.
    6. Hino, Airo & Fahey, Robert A., 2019. "Representing the Twittersphere: Archiving a representative sample of Twitter data under resource constraints," International Journal of Information Management, Elsevier, vol. 48(C), pages 175-184.
    7. Eszter Hargittai, 2015. "Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 63-76, May.
    8. Samuel-Azran, Tal & Hayat, Tsahi (Zack), 2020. "The geography of the Arab public sphere on Twitter," Technology in Society, Elsevier, vol. 62(C).
    9. Fischer, Eileen & Reuber, A. Rebecca, 2011. "Social interaction via new social media: (How) can interactions on Twitter affect effectual thinking and behavior?," Journal of Business Venturing, Elsevier, vol. 26(1), pages 1-18, January.
    10. Emilio Ferrara & Zeyao Yang, 2015. "Measuring Emotional Contagion in Social Media," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
    11. Han, Sehee & Min, Jinyoung & Lee, Heeseok, 2015. "Antecedents of social presence and gratification of social connection needs in SNS: A study of Twitter users and their mobile and non-mobile usage," International Journal of Information Management, Elsevier, vol. 35(4), pages 459-471.
    12. Kristina Lerman & Luciano G. Marin & Megha Arora & Lucas H. Costa Lima & Emilio Ferrara & David Garcia, 2018. "Language, demographics, emotions, and the structure of online social networks," Journal of Computational Social Science, Springer, vol. 1(1), pages 209-225, January.
    13. Gregory Eady & Jonathan Nagler & Andy Guess & Jan Zilinsky & Joshua A. Tucker, 2019. "How Many People Live in Political Bubbles on Social Media? Evidence From Linked Survey and Twitter Data," SAGE Open, , vol. 9(1), pages 21582440198, February.
    14. Mohammed, Abdulalem & Ferraris, Alberto, 2021. "Factors influencing user participation in social media: Evidence from twitter usage during COVID-19 pandemic in Saudi Arabia," Technology in Society, Elsevier, vol. 66(C).
    15. Moshkovitz, Karin & Hayat, Tsahi, 2021. "The rich get richer: Extroverts' social capital on twitter," Technology in Society, Elsevier, vol. 65(C).
    16. Schaarschmidt, Mario & Könsgen, Raoul, 2020. "Good citizen, good ambassador? Linking employees' reputation perceptions with supportive behavior on Twitter," Journal of Business Research, Elsevier, vol. 117(C), pages 754-763.
    17. Tao Lu & Aimee L. Franklin, 2018. "A Protocol for Identifying and Sampling From Proxy Populations," Social Science Quarterly, Southwestern Social Science Association, vol. 99(4), pages 1535-1546, December.
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