On the Validity of Using Webpage Texts to Identify the Target Population of a Survey: An Application to Detect Online Platforms - Piet Daas; Wolter Hassink; Bart Klijs (Institute of Labor Economics (IZA), 2023)
A statistical classification model was developed to identify online platform organizations based on the texts on their website. The model was subsequently used to identify all (potential) platform organizations with a website included in the Dutch Business Register. The empirical outcomes of the statistical model were plausible in terms of the words and the bimodal distribution of fitted probabilities, but the results indicated an overestimation of the number of platform organizations. Next, the external validity of the outcomes was investigated through a survey held under the organizations that were identified as a platform organization by the statistical classification model. The response by the organizations to the survey confirmed a substantial number of type-I errors. Furthermore, it revealed a positive association between the fitted probability of the text-based classification model and the organization's response to the survey question on being an online platform organization. The survey results indicated that the text-based classification model can be used to obtain a subpopulation of potential platform organizations from the entire population of businesses with a website.
Keywords: C81; C83; D20; D83; L20; 330; online platform organizations; external validation; type-I error; machine learning; web pages