Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science
By Hai Liang, King-wa Fu. 2015
49 citations recorded by the source corpus.
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.
Subfields: Communication, Statistical and Nonlinear Physics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Communication
- Alexandra Olteanu
- Fernando Diaz
- Carlos Castillo
- Emre Kıcıman
- Ponnurangam Kumaraguru
- Gabrielle N. Martin
- Amod Kant Agrawal
- David García
In Statistical and Nonlinear Physics
- Carlos Castillo
- Alexandra Olteanu
- Fernando Diaz
- Emre Kıcıman
- Ponnurangam Kumaraguru
- Amod Kant Agrawal
- David García
- Brian H. Spitzberg
Citations in the corpus
- Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, methodology): “For instance, 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.”
- Collective aspects of privacy in the Twitter social network (2018, methodology): “To generate an initial unbiased random sample of users, we applied the Random Digit Search method [24, 25]: We generated random Twitter user ids in the range between 1 and 30 Billion, looked them up through the Twitter REST API,b and saved the basic user information of the valid sampled users.”
- Trends in U.S. Adolescents’ media use, 1976–2016: The rise of digital media, the decline of TV, and the (near) demise of print. (2019, background): “The displacement model conforms to some relatively unassailable facts of communication: Information (Hilbert & López, 2011) and mediated access to such media (Backstrom, Boldi, Rosa, Ugander, & Vigna, 2012; Daraghmi & Yuan, 2014) are increasing, whereas time (Liang & Fu, 2015; Malmgren, Stouffer, Ca”