Robust End-User-Driven Social Media Monitoring for Law Enforcement and Emergency Monitoring
Nowadays social media mining is broadly used in the security sector to support law enforcement and to increase response time in emergency situations. One approach to go beyond the manual inspection is to use text mining technologies to extract latent topics, analyze their geospatial distribution and to identify the sentiment from posts. Although widely used, this approach has proven to be technically difficult for end-users: the language used on social media platforms rapidly changes and the domain varies according to the use case. This paper presents a monitoring architecture that analyses streams from social media, combines different machine learning approaches and can be easily adapted and enriched by user knowledge without the need for complex tuning. The framework is modeled based on the requirements of two H2020-projects in the area of community policing and emergency response.