Political polarization occurs when individuals or groups adopt increasingly extreme political beliefs and attitudes, becoming more entrenched in their positions. Traditionally analysed through surveys, studying these attitudes can be costly and time-consuming, particularly when examining polarized subpopulations such as fringe communities. Recent advances in NLP models offer unprecedented opportunities to study polarization “in the wild,”…

Explanatory learning models in Natural Language Processing (NLP) are models that provide explanations for their predictions or decisions. They help humans understand why a particular decision or prediction was made. This is important because it fosters trust in the system. When humans understand the reasoning behind a decision, they are more likely to trust the…

Text mining and natural language processing (NLP) systems are proving very useful for clinical care and research. Several decisions on patient inclusion/exclusion and coding of key study variables in clinical studies are taken out of the hands of clinicians and put into the care of NLP systems. However, clinical care is not always equitable; for…

Although recent works have applied sophisticated NLP methods to quantify social bias in large corpora , there is still limited research regarding the associations between users’ beliefs and social bias expressed in online text. In this project, we are interested in filling this gap by exploring whether there are any associations between implicit social bias…

Biases and stereotypes in society can be reflected in different sectors of our everyday life including work environment, education and politics. News and political speeches are only two examples of textual content in which stereotypes are present. In this project, we focus on both gender and racial bias. Using approaches from NLP, first we explore…