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Robust End-User-Driven Social Media Monitoring for Law Enforcement and Emergency Monitoring

: Kirsch, Birgit; Giesselbach, Sven; Knodt, David; Rüping, Stefan

Fulltext ()

Leventakis, Georgios:
Community-oriented policing and technological innovations
Cham: Springer International Publishing, 2018 (Springer briefs in criminology)
ISBN: 978-3-319-89293-1 (Print)
ISBN: 978-3-319-89294-8 (Online)
European Commission EC
H2020; 730082; E2MC
Evolution of Emergency Copernicus services
Book Article, Electronic Publication
Fraunhofer IAIS ()
LDA; targeted topic model; crisis response

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.