Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science

Hai Liang · King‐Wa Fu

42 citations0/0 files storedPLoS ONE

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Computational and Text Analysis Methods Complex Network Analysis Techniques Social Media and Politics

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.

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2001Birds of a Feather: Homophily in Social Networks 2009Power-Law Distributions in Empirical Data 2018Power-law distributions in empirical data 2009Computational Social Science 1992Neocortex size as a constraint on group size in primates 2011Political Polarization on Twitter 2013MANUAL OF POLITICALECONOMY 2021Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose

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