Content-Based Covert Group Detection in Social Networks
Abstract
Along with the benefits of providing ways for people to connect and socialize on-line, social media provides opportunities for various groups to spread messages to try to spread influence and shape opinions according to interests of the group. Community detection in social media is a challenging problem. A community can be defined as a group of users that (1) interact with each other more frequently than with those outside the group and (2) are more similar to each other that to those outside the group. Research on detection of groups that disseminate content to push their own agenda faces many challenges, due among many others, to the difficulties of modeling and detecting cues for strategic messages of groups. To help close this research gap, the overall goal of this project is to develop techniques and algorithms for detection of goal-driven covert groups that spread strategic information.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 06, 2017
- Accession Number
- AD1051096
Entities
People
- Katia Sycara
Organizations
- Carnegie Mellon University