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Studying online censorship perception & bias using social media & unsupervised machine learning.
- Format:
- Video
- Series:
- Sage research methods.
- Language:
- English
- Subjects (All):
- Social media--Censorship.
- Social media.
- Mass media--Censorship.
- Mass media.
- Machine learning.
- Quantitative research.
- Physical Description:
- 1 online resource (1 video file (00:15:35)) : sound, colour
- Other Title:
- Studying online censorship perception and bias using social media and unsupervised machine learning
- Place of Publication:
- London : SAGE Publications Ltd, 2019.
- Language Note:
- Closed-captions in English.
- Summary:
- 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.
- Participant:
- Academic, Qinlan Shen.
- Notes:
- Description based on XML content.
- ISBN:
- 9781526489203 (streaming video) :
- OCLC:
- 1091311177
- Access Restriction:
- Restricted for use by site license.
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