Event organised by the Computational Humanities research group.
To register to the seminar, please fill in this form by 23 February 2025.
25 February 2025 – 3pm GMT
Remote – Via Microsoft Teams.
Vjosa Preniqi (Queen Mary University of London), Evolution of Moral Expressions in Music: Applying MoralBERT to Large-Scale Lyrics Datasets
Abstract
Lyrics serve as more than emotional expressions; they act as a powerful medium for reflecting and influencing societal, political, and cultural landscapes. From addressing racial inequality and gender discrimination to mirroring political unrest, song lyrics have historically provided a lens into the social issues of their time. Recent analyses, such as the rising gender bias in lyrics from 1960 to 2010 among male artists on Billboard charts, reveal concerning trends and ethical implications, including the promotion of violence, misogyny, and substance abuse. These findings underscore the importance of understanding the broader moral and cultural messages conveyed in music. In this presentation, I explore the application of MoralBERT, a tool that we built to detect and analyse moral foundations in English song lyrics. By leveraging the extensive WASABI dataset (1960–2010), the presentation will introduce a study that was conducted to examine the evolution of moral expression across genres while highlighting themes such as gender perspectives and societal narratives in music. The findings demonstrate how advanced natural language processing (NLP) models can uncover underlying moral dimensions in lyrics, offering insights into their societal impacts and implications for media literacy.
Bio
Vjosa Preniqi is a PhD Candidate at Queen Mary University of London, based in the Centre for Digital Music (C4DM) within the School of Electronic Engineering and Computer Science. Her research explores the connections between moral values, music content, and listening behaviours, combining insights from data science, AI, musicology and psychology. Passionate about interdisciplinary research, she bridges the gap between data science and the arts to better understand the cultural and emotional impact of music.
