Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?
Paper presentation at ACM FAT* 2020, Barcelona, Spain
Abstract: Many machine learning projects for new application areas involve teams of humans who label data for a particular purpose, from hiring crowdworkers to the paper’s authors labeling the data themselves. Such a task is quite similar to (or a form of) structured content analysis, which is a longstanding methodology in the social sciences and humanities, with many established best practices. In this paper, we investigate to what extent a sample of machine learning application papers in social computing — specifically papers from ArXiv and traditional publications performing an ML classification task on Twitter data — give specific details about whether such best practices were followed.