Muyang Li
Assistant Professor
Office: Vari Hall, 2116
Ext: 33913
Email: muyangli@yorku.ca
Media Requests Welcome
Muyang Li is interested in digital sociology, cultural sociology, authoritarianism, and gender issues. Her research is organized around a key question: how does media interact with democracy and social life? Her recent study adopts a mixed-methods approach to explore the negotiation between the authoritarian state and the public in defining democracy through social media. She is a Faculty Fellow at the Center for Cultural Sociology at Yale University and a Faculty Associate at York Centre for Asian Research.
Degrees
Ph.D. Sociology, University at AlbanyM.Sc. New Media, Chinese University of Hong Kong
B.A. Communication, Communication University of China
Research Interests
Current Research Projects
-
Summary:
This project aims to compare how news outlets in Canada and the U.S. communicate COVID-19 vaccines and the risks of the Coronavirus to the public, and the extent to which cross-ideology news consumption shapes public trust in vaccines.
Description:Principal Investigator, SSHRC Insight Development Grant # 430-2021-01065, $56,967
Start Date:
- Month: Sep Year: 2021
End Date:
- Month: Sep Year: 2023
Funders:
SSHRC
-
Summary:
The conventional thinking about algorithmic harm to democracy emphasizes the detrimental effects of algorithms that have built-in bias or are in some other way inattentive to pre-existing social inequity. Based on this perspective, there is now a common belief that improving algorithms should suffice to solve the problem of algorithmic harm. This, however, is true only to an extent. How algorithms work and how people think algorithms work are two interrelated but distinctive aspects involved in accessing algorithmic harm.
This project introduces a cultural perspective to understand how algorithms could be used against democracy by exploring how algorithmic imaginaries—the way people imagine, perceive, and experience algorithms—are used to develop a particular type of conspiracy theories.
Luo, Z. & Li, M. (2022). Collecting and Analyzing Weibo Data: A Roadmap for Social Research. In The SAGE Handbook of Social Media Research Methods, 2nd ed, SAGE Publications Ltd.
Davis, J. L., Kidd, D., Li, M., Burgese, T. J., Aalders, R. (2022). Information technology & media sociology in a (still) pandemic world. Information, Communication and Society, 25(5), 587-590.
Luo, Z. & Li, M. (2022). Participatory Censorship: How Online Fandom Community Facilitates Authoritarian Rule. New Media & Society, online first.
Yang, Q., Luo, Z., Li, M., & Liu, J. (2021). Understanding the Landscape and Propagation of COVID-19 Misinformation and its Correction on Sina Weibo. Global Health Promotion, 29(1):44-52.
Li, M., & Luo, Z. (2020). The ‘Bad Women Drivers’ Myth: The Overrepresentation of Female Drivers and Gender Bias in China’s Media. Information, Communication and Society, 23(5), 776-793.
Current Courses
| Term | Course Number | Section | Title | Type |
|---|---|---|---|---|
| Fall/Winter 2023 | AP/SOCI1030 6.0 | A | Mediated Life in a Digital World | LECT |
Muyang Li is interested in digital sociology, cultural sociology, authoritarianism, and gender issues. Her research is organized around a key question: how does media interact with democracy and social life? Her recent study adopts a mixed-methods approach to explore the negotiation between the authoritarian state and the public in defining democracy through social media. She is a Faculty Fellow at the Center for Cultural Sociology at Yale University and a Faculty Associate at York Centre for Asian Research.
Degrees
Ph.D. Sociology, University at AlbanyM.Sc. New Media, Chinese University of Hong Kong
B.A. Communication, Communication University of China
Research Interests
Current Research Projects
-
Summary:
This project aims to compare how news outlets in Canada and the U.S. communicate COVID-19 vaccines and the risks of the Coronavirus to the public, and the extent to which cross-ideology news consumption shapes public trust in vaccines.
Description:Principal Investigator, SSHRC Insight Development Grant # 430-2021-01065, $56,967
Project Type: FundedRole: Principal Investigator
Start Date:
- Month: Sep Year: 2021
End Date:
- Month: Sep Year: 2023
Funders:
SSHRC
-
Summary:
The conventional thinking about algorithmic harm to democracy emphasizes the detrimental effects of algorithms that have built-in bias or are in some other way inattentive to pre-existing social inequity. Based on this perspective, there is now a common belief that improving algorithms should suffice to solve the problem of algorithmic harm. This, however, is true only to an extent. How algorithms work and how people think algorithms work are two interrelated but distinctive aspects involved in accessing algorithmic harm.
This project introduces a cultural perspective to understand how algorithms could be used against democracy by exploring how algorithmic imaginaries—the way people imagine, perceive, and experience algorithms—are used to develop a particular type of conspiracy theories.
All Publications
Luo, Z. & Li, M. (2022). Collecting and Analyzing Weibo Data: A Roadmap for Social Research. In The SAGE Handbook of Social Media Research Methods, 2nd ed, SAGE Publications Ltd.
Davis, J. L., Kidd, D., Li, M., Burgese, T. J., Aalders, R. (2022). Information technology & media sociology in a (still) pandemic world. Information, Communication and Society, 25(5), 587-590.
Luo, Z. & Li, M. (2022). Participatory Censorship: How Online Fandom Community Facilitates Authoritarian Rule. New Media & Society, online first.
Yang, Q., Luo, Z., Li, M., & Liu, J. (2021). Understanding the Landscape and Propagation of COVID-19 Misinformation and its Correction on Sina Weibo. Global Health Promotion, 29(1):44-52.
Li, M., & Luo, Z. (2020). The ‘Bad Women Drivers’ Myth: The Overrepresentation of Female Drivers and Gender Bias in China’s Media. Information, Communication and Society, 23(5), 776-793.
Current Courses
| Term | Course Number | Section | Title | Type |
|---|---|---|---|---|
| Fall/Winter 2023 | AP/SOCI1030 6.0 | A | Mediated Life in a Digital World | LECT |
