Forensic Analysis of Heterogeneous Social Media Data

A. Nikolaidou
M. Lazaridis
T. Semertzidis
A. Axenopoulos
P. Daras
In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), pp 343-350, Vienna, Austria, September 17-19, 2019.


It is a challenge to aggregate and analyze data from heterogeneous social media sources not only for businesses and organizations but also for Law Enforcement Agencies. The latter's core objectives are to monitor criminal and terrorist related activities and to identify the "key players" in various networks. In this paper, a framework for homogenizing and exploiting data from multiple sources is presented. Moreover, as part of the framework, an ontology that reflects today's social media perceptions is introduced. Data from multiple sources is transformed into a labeled property graph and stored in a graph database in a homogenized way based on the proposed ontology. The result is a cross-source analysis system where end-users can explore different scenarios and draw conclusions through a library of predefined query placeholders that focus on forensic investigation. The framework is evaluated on the Stormfront dataset, a radical right, web community. Finally, the benefits of applying the proposed framework to discover and visualize the relationships between the Stormfront profiles are presented.