An artificial intelligence framework for transforming unstructured text into structured survey data

We propose an original methodology for converting unstructured textual data into structured survey data through the use of chatbot technologies. Specifically, we employ ChatGPT application programming interface tools to associate a given short text – interpreted as a potential response – with the most probable question, selected from a predefined list, that could plausibly have elicited it. This list may correspond to items from a survey questionnaire. In addition, we instructed ChatGPT to identify an appropriate response option in line with the semantic content of the text. The input text may originate from (i) a post on the X platform (formerly Twitter) or (ii) an open-ended response within an actual survey questionnaire. In the former case, the method enables the construction of a structured survey dataset from social media messages; in the latter, it provides a means to assess the reliability of responses in an existing survey instrument. Our findings highlight the potential of chatbot-based methods to enhance the integration of textual and survey data, offering new opportunities for constructing timely indicators and for improving the assessment of response quality in existing survey instruments. More broadly, the approach contributes to advancing mixed-data methodologies in social research.