期刊
SOCIAL SCIENCE COMPUTER REVIEW
卷 40, 期 6, 页码 1496-1522出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/08944393211012268
关键词
social media analytics; benchmarking; social computing; reproducibility
类别
资金
- German Federal Ministry of Education and Research [FKZ 16KIS0495K]
- Ministry of Culture and Science of the German State of North Rhine-Westphalia [FKZ 005-1709-0001, EFRE-0801431, FKZ 005-1709-0006]
- European Research Center for Information Systems (ERCIS)
- DAAD PPP Germany-Australia [57511656]
- EU H2020 Program under the scheme INFRAIA-01-2018-2019: Research and Innovation action grant [871042]
Computational social science evaluates social interaction using computational and statistical methods, with public availability of data sets being essential for reliable research. Restrictions on data sharing for social media analytics research create challenges for replicability, prompting the proposal of a new evaluation framework to address these issues.
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. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are 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.
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