  Twitter data have become essential to Natural Language Processing (NLP) and
social science research, driving various scientific discoveries in recent
years. However, the textual data alone are often not enough to conduct studies:
especially social scientists need more variables to perform their analysis and
control for various factors. How we augment this information, such as users'
location, age, or tweet sentiment, has ramifications for anonymity and
reproducibility, and requires dedicated effort. This paper describes
Twitter-Demographer, a simple, flow-based tool to enrich Twitter data with
additional information about tweets and users. Twitter-Demographer is aimed at
NLP practitioners and (computational) social scientists who want to enrich
their datasets with aggregated information, facilitating reproducibility, and
providing algorithmic privacy-by-design measures for pseudo-anonymity. We
discuss our design choices, inspired by the flow-based programming paradigm, to
use black-box components that can easily be chained together and extended. We
also analyze the ethical issues related to the use of this tool, and the
built-in measures to facilitate pseudo-anonymity.
