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Net activism and whistleblowing on YouTube: a text mining analysis.
1Guangdong University of Foreign Studies (GDUFS), Guangzhou, China.
Multimedia Tools and Applications
|October 4, 2022
Summary
This study analyzed YouTube whistleblowing videos, creating a cleaned dataset for social computational science. Findings include a topic hierarchy and a 57.4% accurate whistleblowing detection model.
Area of Science:
- Computational Social Science
- Digital Sociology
- E-Democracy
Background:
- Social media, particularly YouTube, is a significant data source for understanding societal issues.
- Net activism and whistleblowing are crucial topics within online discourse.
Purpose of the Study:
- To create and analyze a large-scale dataset of YouTube videos related to whistleblowing.
- To develop methods for topic modeling, whistleblowing detection, and contextual analysis of whistleblower mentions.
Main Methods:
- Web scraping to collect 644,575 video transcriptions.
- Automated linguistic feature extraction and clustering for data cleaning and topic identification.
- Lexical representation learning and manual interpretation for topic hierarchy and marker analysis.
Main Results:
- A cleaned dataset of 35,730 video transcriptions was curated.
- A hierarchy of whistleblowing topics was identified.
- Whistleblowing video detection achieved 57.4% accuracy.
- Contextual linguistic markers around whistleblower names were semi-automatically extracted.
Conclusions:
- The developed methods and dataset facilitate digital sociology and e-democracy research.
- Automated analysis of social media content can reveal insights into online activism and whistleblowing.
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