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Replication of previous Twitter studies revealed 60% of claims could not be verified due to data and analysis variations. This research offers a new method for sampling Twitter users for social science research.

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Area of Science:

  • Social Sciences
  • Computational Social Science
  • Data Science

Background:

  • Replication is crucial for scientific validity.
  • Previous studies on Twitter have generated several propositions.
  • Assessing the replicability of these propositions is necessary.

Purpose of the Study:

  • To generalize and replicate 10 propositions from prior Twitter studies.
  • To evaluate the robustness of existing claims using a representative dataset.
  • To propose a novel method for sampling Twitter users and ego networks.

Main Methods:

  • Systematic assessment of 10 propositions from previous Twitter research.
  • Utilized a representative dataset for generalization and replication attempts.
  • Developed a feasible approach for random sampling of Twitter users and ego networks.

Main Results:

  • Six out of ten propositions could not be replicated.
  • Replication failures were attributed to variations in data collection, analytical strategies, and measurement inconsistencies.
  • A novel sampling methodology for individual-level social science inquiry on Twitter was proposed.

Conclusions:

  • Many claims from prior Twitter studies lack replicability.
  • Methodological variations significantly impact research findings.
  • The proposed sampling method offers a potential solution for individual-level analysis on Twitter without full data access.