Date: February 06, 2015
Time: 13:00 – 14:00
Venue: Digital Media Lab, G/F, Eliot Hall, JMSC, HKU
Replication is an essential requirement for scientific discovery. The current study aims to generalise 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.
Dr. Hai Liang is a Postdoctoral Fellow at the Journalism and Media Studies Centre at The University of Hong Kong. His research interest and background cross science and social science, ranging from social-web data-mining to political deliberation and computational advertising. Currently, he is working on several interdisciplinary projects at the intersection of computational science and online human communication. Dr. Hai Liang received his PhD from the Web Mining Laboratory at City University of Hong Kong. He got two bachelor degrees from Shanghai Jiao Tong University, one in Media and Communication and another in Mathematics.