Accession Number:

AD1051096

Title:

Content-Based Covert Group Detection in Social Networks

Descriptive Note:

Technical Report,01 Oct 2013,31 May 2017

Corporate Author:

Carnegie Mellon University Pittsburgh United States

Personal Author(s):

Report Date:

2017-09-06

Pagination or Media Count:

15.0

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.

Subject Categories:

  • Information Science

Distribution Statement:

APPROVED FOR PUBLIC RELEASE