PriPA: a tool for privacy-preserving analytics of linguistic data

The days of large amorphous corpora collected with armies of Web crawlers and stored indefinitely are, or should be, coming to an end. There is a wealth of hidden linguistic information which is increasingly difficult to access, hidden in personal and private data that would be unethical and technically challenging to collect using traditional methods such as Web crawling and mass surveillance of online discussion spaces. Advances in privacy regulations such as GDPR and changes in the zeitgeist bring into question the problematic ethical dimension of extracting information from unaware if not unwilling participants. Modern corpora need to adapt, be focused on testing specific hypotheses, and be respectful of the privacy of the people who generated its data. Our work focuses on using a distributed participatory approach and continuous informed consent to solve these issues, by allowing participants to voluntarily contribute their own censored personal data at a granular level. We evaluate our approach in a three-pronged manner, testing the accuracy of measurement of statistical measures of language with respect to standard corpus linguistics tools, evaluating the usability of our application with a participant involvement panel, and using the tool for a case study on health communication