Home // International Journal On Advances in Intelligent Systems, volume 10, numbers 3 and 4, 2017 // View article

SoNA: A Knowledge-based Social Network Analysis Framework for Predictive Policing

Authors:
Michael Spranger
Hanna Siewerts
Joshua Hampl
Florian Heinke
Dirk Labudde

Keywords: forensic; opinion-leader; topic mining; expert system; text analysis; classification; sentiment analysis

Abstract:
Major incidents can disturb the state of balance of a society and it is important to increase the resilience of the society against such disturbances. There are different causes for major incidents, one of which are groups of individuals, for example at demonstrations. The ideal way to handle such events would be to prevent them, or at least provide information to ensure the appropriate security services are prepared. Nowadays, a lot of communication, even criminal, takes place in social networks, which, hence, provide the ideal ground to gain the necessary information, by monitoring such groups. In the present paper, we propose an application framework for knowledge-based social network monitoring. The ultimate goal is the prediction of short-term activities, as well as the long-term development of potentially dangerous groups, based on sentiment and topic analysis and the identification of opinion-leaders. Here, we present the first steps to reach this goal, which include the assessment of the risk for a major incident caused by a group of individuals based on the sentiment in the social network groups and the topics discussed.

Pages: 147 to 156

Copyright: Copyright (c) to authors, 2017. Used with permission.

Publication date: December 31, 2017

Published in: journal

ISSN: 1942-2679