言外之意:一种混合RNN架构来检测电影中的偏见

Shahana Nandy, Ankith Suresh
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引用次数: 0

摘要

看电影被认为是当今最流行的消遣方式。考虑到有研究表明青少年从电影中吸收社会行为,我们提出了一个混合深度学习模型来检测电影中的偏见程度(性别歧视、种族主义、残疾歧视、仇外心理、同性恋恐惧症等)。使用堆叠bi-LSTM和XGBoost模型进行预测,我们比较了两个时间段(1975-2000年和2001-2020年)电影中偏见的百分比。此外,我们还将迪士尼、皮克斯等制作公司制作的动画电影与被电影内容分级委员会(Motion Picture Content Rating board)评为R级或PG-18级的电影进行了比较。结果显示,在最近的几十年里,偏见的比例已经大大减少了,而且令人惊讶的是,面向年轻目标观众的电影,平均而言,比那些面向青少年和成年人的电影更容易产生偏见。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reading between the lines: A hybrid RNN architecture to detect prejudice in movies
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.
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