Qualitative Code Suggestion: A Human-Centric Approach to Qualitative Coding
Submission Type: Regular Long Paper
Submission Track: Human-Centered NLP
Submission Track 2: Computational Social Science and Cultural Analytics
Keywords: qualitative coding, human-centric
TL;DR: We approach qualitative coding in a human-centric fashion and show that this creates technical challenges which lead us to experiment with systems that can suggest relevant codes for highlighted passages.
Abstract: Qualitative coding is a content analysis method in which researchers read through a text corpus and assign descriptive labels or qualitative codes to passages. It is an arduous and manual process which human-computer interaction (HCI) studies have shown could greatly benefit from NLP techniques to assist qualitative coders. Yet, previous attempts at leveraging language technologies have set up qualitative coding as a fully automatable classification problem. In this work, we take a more assistive approach by defining the task of qualitative code suggestion (QCS) in which a ranked list of previously assigned qualitative codes is suggested from an identified passage. In addition to being user-motivated, QCS integrates previously ignored properties of qualitative coding such as the sequence in which passages are annotated, the importance of rare codes and the differences in annotation styles between coders. We investigate the QCS task by releasing the first publicly available qualitative coding dataset, CVDQuoding, consisting of interviews conducted with women at risk of cardiovascular disease. In addition, we conduct a human evaluation which shows that our systems consistently make relevant code suggestions.
Submission Number: 3631
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