Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science.
Where this comes from
- Record sourced from PubMed, PMID 26287530.
- Also identified by DOI 10.1371/journal.pone.0134270 and PMC identifier 4546006.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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.
Medical subject headings
- Social Media