Testing propositions derived from twitter studies: generalization and replication in computational social science
Open accessAbstract
Replication is an essential requirement for scientific discovery. The current study aims to generalize and replicate 10 propositions made in previous Twitter studies using a representative dataset. Our findings suggest 6 out of 10 propositions could not be replicated due to the variations of data collection, analytic strategies employed, and inconsistent measurements. The study's contributions are twofold: First, it systematically summarized and assessed some important claims in the field, which can inform future studies. Second, it proposed a feasible approach to generating a random sample of Twitter users and its associated ego networks, which might serve as a solution for answering social-scientific questions at the individual level without accessing the complete data archive.
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Due to variations in the analysis methodology, measurement, and data collection, Liang and Fu (2015) could not replicate 6 out of 10 known propositions from social media studies.
“due to variations in the analysis methodology, measurement, and data collection, Liang and Fu (2015) could not replicate 6 out of 10 known propositions from social media studies.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Liang and Fu, 2015
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- Authors
- Liang, H., & Fu, K.-W.
- Affiliations
- University of Hong Kong (HK)
- Corresponding author
- Fu, King-wa
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-08-19
- Volume
- 10
- Issue
- 8
- Pages
- e0134270
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4546006
- OpenAlex
- W1819637045
- Cited by
- 42
- References
- 56
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 5 · 2023: 3 · 2022: 5 · 2021: 2 · 2019: 7 · 2018: 10 · 2017: 4 · 2016: 3
- FWCI
- 32.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion, cc-by-nc-nd)
- Funders
- University of Hong Kong (201409176011)
- Hong Kong Government
- Topics
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Communication › Social Media and Politics
- Keywords
- Twitter, replication, generalization, propositions, representative sampling, personal networks, measurement discrepancy, computational social science
- MeSH terms
- Attention, Circadian Rhythm, Data Collection, Ego, Humans, Social Sciences, Information Dissemination, Social Media
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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