Dismantling Gender Blindness in Online Discussion of a Crime/Gender Dichotomy Yigang Qin, Weilun Duan, Qunfang Wu, Zhicong Lu. (2024).
Proceedings of the ACM on Human-Computer Interaction (CSCW). p 1-31.
DOI

Keywords: Public Opinion, Online Activism, Feminism, Gender-blind Sexism, Crime, Weibo, Topic Modeling, Discourse Analysis

Contemporary feminists utilize social media for activism, while backlashes come along. The gender-related discourses are often diminished when addressing public events regarding sexism and gender inequality on social media platforms. The dichotomous debate around the Tangshan beating incident in China epitomized how criminal interpretations of gender-related violence became a backlash against feminist expressions. By analyzing posts on Weibo using mixed methods, we describe the emerging discursive patterns around crime and gender, uncovering the gender-blind sexism that refutes feminist discourses on the social platform. We also highlight the critical hurdles facing grassroots feminist activism in Chinese cyberspace and propose implications for the design and research related to digital feminist activism.

Generating Natural Language Responses in Robot-Mediated Referential Communication Tasks to Simulate Theory of Mind Ziming Liu, Yigang Qin, Huiqi Zou, Eun Jin Paek, Devin Casenhiser, Wenjun Zhou, and Xiaopeng Zhao. (2022).
International Conference on Social Robotics (ICSR). p 100-109.
DOI · Full Paper

Keywords: Human-Robot Interaction, Theory of Mind, Social Assistive Robots, Natural Language Processing

With advances in neural network-based computation, socially assistive robots have been endowed with the ability to provide natural conversation to users. However, the lack of transparency in the computation models results in unexpected robot behaviors and feedback, which may cause users to lose their trust in the robot. Theory of mind (ToM) in cooperative tasks has been considered as a key factor in understanding the relationship between user acceptance and the explainability of robot behaviors. Therefore, we develop a dialog system using previously collected data from a robot-mediated cooperative communication task data to simulate natural language smart feedback. The system is designed based on the mechanism of ToM and validated with a simulation test. Based on the result, we believe the designed dialog system bears the feasibility of simulating ToM and can be used as a research tool for further studying the importance of simulating ToM in human-robot communication.