Can a Computer Recognize Hate Speech? Machine Learning (ML) in Qualitative Data Analysis
DOI:
https://doi.org/10.18778/1733-8069.13.2.04Keywords:
machine learning, qualitative data analysis, hate speech, intercoder agreementAbstract
The purpose of this article is to present the process of automatic tagging of hate speech in social media. The implementation of this process allows for quantitative treatment of qualitative methods: analysis on the corpora of hundreds thousands of texts based on their meaning. The process is possible through algorithms of machine learning (ML). The example of the hate speech designation project in texts from Polish online forums is presented. The key issue is the precise of conceptualization and operationalization of category “hate speech.” This allows for preparing specific instructions and conducting the training code unit. As a result we get higher rates of inter-coder agreement. Marked texts will be used as training data for automated categorization methods based on ML algorithms. Then we describe the course of machine coding. This article also seeks to establish problems associated with automatic coding of hate speech and propose solutions. In summary, we point the factors that are crucial to the research process that uses machine learning.
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