Detection and Analysis of Gender Bias in Media through NLP Techniques

Resumen

Gender inequality remains a pervasive global issue, with the struggle for women's rights gaining particular momentum in the latter half of the 20th century and into this century. The \textit{Global Gender Gap Report} evaluates gender parity across several dimensions. Despite progress, no country has achieved full gender parity. However, Spain is actively pursuing equality in key sectors. Media representation is crucial for normalising female leadership and recognising women’s contributions. Against this backdrop, this article presents a computational system to analyse gender representation in the Spanish press using Natural Language Processing (NLP) techniques, such as Named Entity Recognition (NER), quote extraction and coreference resolution. The system performs web scraping, identifies quotations, links them to speakers and predicts their gender to measure current representation. With a methodological focus on text pre-processing and quotation extraction, the findings highlight the usefulness of NLP for large-scale analysis, supporting efforts towards gender equality.