Shadi Shajari
Biographic Data
| ID | 1904106 |
|---|---|
| NAME | Shadi Shajari |
| GIVEN NAMES | Shadi |
| FAMILY NAME | Shajari |
| SIGNATURE | SHAJARI S |
| AFFILIATIONS | University of Arkansas at Little Rock |
| ORCID | 0009-0001-8500-1146 |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Safeguarding youtube discussions
In today’s digital landscape, YouTube’s comment sections play an essential role in shaping public opinion and discussions. However, coordinated groups often exploit these platforms to spread misinformation and distort conversations. This study introduces a method to detect and quantify such anomalous behavior on YouTube channels by analyzing two key components: commenter behaviors and engagement patterns. Focusing on channels concerning news outl…
Developing a network-centric approach for anomalous behavior detection on youtube
As the second most visited website globally, YouTube serves as a central platform for video sharing, entertainment, and information dissemination. However, its expansive and highly active user base also facilitates problematic behavior, particularly among commenters. This study presents a methodology driven by social network analysis to detect and examine anomalous commenter behaviors, with a specific focus on commenter mobs that collaborate to a…
No prominent works on this page.
Safeguarding youtube discussions
In today’s digital landscape, YouTube’s comment sections play an essential role in shaping public opinion and discussions. However, coordinated groups often exploit these platforms to spread misinformation and distort conversations. This study introduces a method to detect and quantify such anomalous behavior on YouTube channels by analyzing two key components: commenter behaviors and engagement patterns. Focusing on channels concerning news outl…
Developing a network-centric approach for anomalous behavior detection on youtube
As the second most visited website globally, YouTube serves as a central platform for video sharing, entertainment, and information dissemination. However, its expansive and highly active user base also facilitates problematic behavior, particularly among commenters. This study presents a methodology driven by social network analysis to detect and examine anomalous commenter behaviors, with a specific focus on commenter mobs that collaborate to a…
Computer Science (2 works) · Misinformation and Its Impacts (2 works) · Anomaly Detection Techniques and Applications (1 work) · Computer network (1 work) · Data science (1 work) · Hate Speech and Cyberbullying Detection (1 work) · Knowledge management (1 work) · Medicine (1 work) · Network Security and Intrusion Detection (1 work) · Safeguarding (1 work)