Carnegie Mellon University PhD candidate, Qinlan Shen, discusses her online censorship perception and bias research using social media and unsupervised machine learning, including what prompted the research, data collection methods, challenges faced, and research still to come.
- Product: SAGE Research Methods Video: Data Science, Big Data Analytics, and Digital Methods
- Type of Content: Case Study
- Title: Studying Online Censorship Perception & Bias Using Social Media & Unsupervised Machine Learning
- Publisher: SAGE Publications, Ltd.
- Publication year: 2019
- Online pub date:
- Discipline: Communication and Media Studies, Political Science and International Relations, Sociology
- Methods: Unsupervised learning, Social media analytics, Computational social science
- Duration: 00:15:35
- Keywords: censored data, censorship, computer science, data analysis, data visualisation, demographic analysis, internet data collection, linguistics, political debates, quantitative content analysis, research, Social media, unsupervised classification
Academic: Qinlan ShenOnline ISBN:9781526489203Copyright: SAGE Publications Ltd., 2019