  Gender bias represents a form of systematic negative treatment that targets
individuals based on their gender. This discrimination can range from subtle
sexist remarks and gendered stereotypes to outright hate speech. Prior research
has revealed that ignoring online abuse not only affects the individuals
targeted but also has broader societal implications. These consequences extend
to the discouragement of women's engagement and visibility within public
spheres, thereby reinforcing gender inequality. This thesis investigates the
nuances of how gender bias is expressed through language and within language
technologies. Significantly, this thesis expands research on gender bias to
multilingual contexts, emphasising the importance of a multilingual and
multicultural perspective in understanding societal biases. In this thesis, I
adopt an interdisciplinary approach, bridging natural language processing with
other disciplines such as political science and history, to probe gender bias
in natural language and language models.
