The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Understanding the Demographics of Twitter Users
Open accessAbstract
Every second, the thoughts and feelings of millions of people across the world are recorded in the form of 140-character tweets using Twitter. However, despite the enormous potential presented by this remarkable data source, we still do not have an understanding of the Twitter population itself: Who are the Twitter users? How representative of the overall population are they? In this paper, we take the first steps towards answering these questions by analyzing data on a set of Twitter users representing over 1% of the U.S. population. We develop techniques that allow us to compare the Twitter population to the U.S. population along three axes (geography, gender, and race/ethnicity), and find that the Twitter population is a highly non-uniform sample of the population.
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Citations
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
Measuring user influence in twitter: The million follower fallacy
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14033 · Cited by 3,022
Cited by
Birds of the Same Feather Tweet Together. Bayesian Ideal Point Estimation Using Twitter Data
Barbera, P. (2012) · doi:10.2139/ssrn.2108098 · Cited by 983
Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data
Barberá, P. (2015) · Political Analysis · doi:10.1093/pan/mpu011 · Cited by 982
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Varol, O., Ferrara, E., Davis, C., Menczer, F., & Flammini, A. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14871 · Cited by 981
“Features extracted from user meta-data have been used to classify users and patterns before (Mislove et al. 2011; Ferrara et al. 2016a).”
MethodSocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763
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
Metadata
- Authors
- Mislove, A., Lehmann, S., Ahn, Y.-Y., Onnela, J.-P., & Rosenquist, J.
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2021-08-03
- Volume
- 5
- Issue
- 1
- Pages
- 554-557
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W2167102709
- Cited by
- 759
- References
- 2
- FWCI
- 70.83
- Type
- conference paper
- Language
- en
- Open access
- diamond
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