Abstract: This manuscript helps to resolve the ongoing debate concerning the effect of information communication technology on human rights monitoring. We reconceptualize human rights as a taxonomy of nested rights that are judged in textual reports and argue that the increasing density of available information should manifest in deeper taxonomies of human rights. With a new automated system, using supervised learning algorithms, we are able to extract the implicit taxonomies of rights that were judged in texts by the US State Department, Amnesty International, and Human Rights Watch over time. Our analysis provides new, clear evidence of change in the structure of these taxonomies as well as in the attention to specific rights and the sharpness of distinctions between rights. Our findings bridge the natural language processing and human rights communities and allow a deeper understanding of how changes in technology have affected the recording of human rights over time.