Reading between the lines: A hybrid RNN architecture to detect prejudice in movies
Shahana Nandy, Ankith Suresh · 2022 7th International Conference on Computer and Communication Systems (ICCCS) · 2022
Watching movies is considered the most popular pastime today. Keeping in mind research that has indicated that adolescents imbibe social behavior from movies, we propose a hybrid deep learning model to detect the degree of prejudice (sexism, racism, ableism, xenophobia, homophobia etc) in movies. Using a stacked bi-LSTM and XGBoost model to make predictions, we compared the percentage of prejudice in movies from two time periods (1975-2000 and 2001-2020). Further, we compared animated movies from production houses like Disney, Pixar with movies often rated R or PG-18 by Motion Picture Content Rating Boards. The results showed that the percentage of prejudice has been significantly reduced in the newer decades, and that, surprisingly, movies intended for a younger target audience, on average, perpetuate more prejudice than those meant for late teenagers and adults.