Benchmarking Crisis in Social Media Analytics: A Solution for the Data Sharing Problem

Dennis Assenmacher, Derek Weber, Mike Preuss, André Calero Valdez, Alison Bradshaw, Björn Ross, Stefano Cresci, Heike Trautmann, Frank Neumann, Christian Grimme

Research output: Contribution to journalArticlepeer-review

Abstract

Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. This data allows researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data is concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design.
Original languageEnglish
Number of pages27
JournalSocial science computer review
Early online date21 May 2021
DOIs
Publication statusE-pub ahead of print - 21 May 2021

Keywords

  • Social Media Analytics
  • Benchmarking
  • Social Computing
  • Reproducibility

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