Discerning Group Biases in Online Communities via Linguistic Analysis

Cultural models are shared understandings of the world by particular cultures, which help individuals interpret the world around them. These models help provide people with internal mental heuristics for what is right, what is just, what is edible, among others. Understanding these models helps us to better understand the nature of the cultures which adopt them, as they provide rules for everyday life. The underlying mechanisms for these models are currently collected through ethnographic analysis undertaken by social scientists using specialized tools like interviews with key informants, pile sorts, and other techniques to better extract the underlying workings of a culture. While social scientists can use specialized tools like interviews with key informants, pile sorts, this “thick data” about the cultures being studied. It is cumbersome and time-consuming, and often requires months to years of fieldwork from social scientists to understand these models and can not easily scale across different types of stereotypes. Fortunately, these shared understandings are redundancies that can be exploited by machine learning techniques to quickly understand the underlying principles of a culture. In the past decade, enormous quantities of textual data, including product reviews, discussion forums, and digital contents, have been generated by online communities. This provides an opportunity and challenge for computer scientists and social scientists to study the underlying group biases present in the online content at scale. The primary scientific goal of the project is to develop a computational framework for rapid data-driven ethnographic analysis of online communities using statistical machine learning and computational linguistic tools. This work is carried out with funding from the DARPA UGB program, HR0011890019.

Visit project