Polarization of public opinions on feminism in China - Jinghua Piao; Fang Zhang; Tianpei Ren; Fengli Xu; Jinglei Zhou; Jun Su; Peng Ru; Yong Li (Humanities & Social Sciences Communications)
Abstract Global efforts on feminism and women’s equality are undergoing intense challenges under the transformative impact of social media and artificial intelligence (AI). This study explores the polarization of public opinions on feminism—a prevalent yet underexplored phenomenon—by analyzing large-scale empirical data from one of China’s most prominent short video platforms. Facilitated by a fine-tuned large language model, we assess 62K users’ opinions on feminism based on their 67M fine-grained behavioral records over two years. We observe a severe polarization of opinions on feminism, where users are asymmetrically polarized into two opposing camps—54.3% in the conservative camp and 34.4% in the liberal camp. We further reveal that the AI-driven recommendation algorithm contributes to two primary drivers of this polarization: (i) echo chambers, where the algorithmic gathering of like-minded users reinforces their shared similar opinions and (ii) battlefields, where opposing users conflict, strengthening pre-existing opinions. We point out that these two drivers are asymmetrically applied to the polarized camps, with conservatives more prone to echo chambers while liberals are more inclined to battlefields. Our study not only uncovers the polarization of public opinions on feminism and its drivers but also offers implications for reducing polarization on increasingly AI-dependent social media.