Trends and Changes in the Field of HCI the Last Decade from the Perspective of HCII Conference

Valdez, André Calero; Ziefle, Martina

doi:10.1007/978-3-030-22646-6_3

Cited by 1 publication

(1 citation statement)

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“…Another feature is the ability to fork (or duplicate) projects to facilitate replication efforts. Many digital analysis tools interact well with the data API of the OSF (Calero Valdez, 2020). Another important criterion for choosing a repository is the financial operating plan.…”

Section: Benchmarking Approaches In the Wildmentioning

confidence: 99%

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

Assenmacher

Weber

Preuß

et al. 2021

Social Science Computer Review

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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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Section: Benchmarking Approaches In the Wildmentioning

confidence: 99%