Jordan Yono
Papers
1
Total Citations
3
H-Index
1
About
Jordan Yono is a researcher focused on the intersection of machine learning and social media integrity, with a particular emphasis on detecting automated accounts. Their most-cited work, "Machine Learning Techniques to Evaluate Whether Twitter Accounts Are Human or Robot" (2020), addresses the growing threat of bot accounts designed to manipulate public opinion, impersonate humans, and exploit social platforms. By applying advanced classification models, Yono’s research provides critical tools for identifying inauthentic behavior, contributing to the broader effort to safeguard online discourse. Though their citation count is modest at 3, the work’s relevance to pressing issues like disinformation and platform security highlights its potential for future impact. Yono’s contributions are especially valuable for students and researchers studying computational social science, cybersecurity, or ethical AI, as they offer a practical framework for tackling real-world challenges in digital trust. Their focus on scalable detection methods underscores a commitment to making social media safer and more transparent.
Research Focus
Key Achievements
Top Papers
- 1