Is bigger always better? potential biases of big data derived from social network sites
Abstract
This article discusses methodological challenges of using big data that rely on specific sites and services as their sampling frames, focusing on social network sites in particular. It draws on survey data to show that people do not select into the use of such sites randomly. Instead, use is biased in certain ways yielding samples that limit the generalizability of findings. Results show that age, gender, race/ethnicity, socioeconomic status, online experiences, and Internet skills all influence the social network sites people use and thus where traces of their behavior show up. This has implications for the types of conclusions one can draw from data derived from users of specific sites. The article ends by noting how big data studies can address the shortcomings that result from biased sampling frames.
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Demographic attributes such as age, gender, race, socioeconomic status, and Internet literacy correlate with how likely someone is to use a social platform.
“In other words, it shows that individuals do not randomly self-select when using social media platforms (Hargittai, 2015), with demographic attributes such as age, gender, race, socioeconomic status, and Internet literacy correlating with how likely someone is to use a social platform.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Hargittai, 2015
Individuals do not randomly self-select when using social media platforms.
“In other words, it shows that individuals do not randomly self-select when using social media platforms (Hargittai, 2015), with demographic attributes such as age, gender, race, socioeconomic status, and Internet literacy correlating with how likely someone is to use a social platform.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Hargittai, 2015
Cites
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206 · see citing works
Predicting the future with social media
Asur, S., & Huberman, B. A. (2010) · 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology · doi:10.1109/wi-iat.2010.63 · Cited by 2,081 · see citing works
Connection strategies: Social capital implications of Facebook-enabled communication practices
Ellison, N. B., Steinfield, C., & Lampe, C. (2011) · New Media & Society · doi:10.1177/1461444810385389 · Cited by 1,349 · cited 1× · see citing works
“However, research has shown this not to be the case (Ellison, Steinfield, and Lampe 2011; Hargittai and Hsieh 2010b); these sites serve a different purpose in various people’s lives resulting in more or less data about certain types of behaviors not because people do not engage in them at all…”
BackgroundData not seen: The uses and shortcomings of social media metrics
Baym, N. K. (2013) · First Monday · doi:10.5210/fm.v18i10.4873 · Cited by 199 · see citing works
Hargittai, E., & Litt, E. (2013) · IEEE Security & Privacy · doi:10.1109/msp.2013.64 · Cited by 79 · see citing works
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Critical analysis of Big Data challenges and analytical methods
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.001 · Cited by 2,066 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
“7 In other words, it shows that individuals do not randomly self-select when using social media platforms (Hargittai, 2015), with demographic attributes such as age, gender, race, socioeconomic status, and Internet literacy correlating with how likely someone is to use a social platform.”
BackgroundFrom internet access to internet skills: digital inequality among older adults
Hargittai, E., Piper, A. M., & Morris, M. R. (2019) · Universal Access in the Information Society · doi:10.1007/s10209-018-0617-5 · Cited by 616 · see citing works
Self-citationA Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media
Chancellor, S., Birnbaum, M. L., Caine, E. D., Silenzio, V. M. B., & De Choudhury, M. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287587 · Cited by 214 · see citing works
Neff, G., Tanweer, A., Fiore-Gartland, B., & Osburn, L. (2017) · Big Data · doi:10.1089/big.2016.0050 · Cited by 173 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works
Koiranen, I., Koivula, A., Keipi, T., & Saarinen, A. (2019) · Telematics and Informatics · doi:10.1016/j.tele.2018.11.009 · Cited by 32 · see citing works
- Authors
- Hargittai, E.
- Corresponding author
- Hargittai, Eszter
- ORCID
- Hargittai, Eszter
- Venue
- The ANNALS of the American Academy of Political and Social Science
- Publisher
- SAGE Publications
- Published
- 2015-04-09
- Volume
- 659
- Issue
- 1
- Pages
- 63-76
- ISSN
- 0002-7162 (ISSN-L)
- 1552-3349
- OpenAlex
- W2053401309
- Cited by
- 268
- References
- 5
- Citations by year
- 2026: 12 · 2025: 21 · 2024: 12 · 2023: 16 · 2022: 19 · 2021: 28 · 2020: 33 · 2019: 48 · 2018: 25 · 2017: 23 · 2016: 27 · 2015: 2 · 2014: 1 · 2012: 1
- FWCI
- 85.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Annals of the American Academy of Political and Social Science (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Social Sciences › Communication › Social Media and Politics
- Keywords
- big data, social network sites, sampling bias, generalizability, socioeconomic status, internet skills, online experience
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