Mining Exceptional Social Behaviour

Conference Object
Change log
Jorge, CC 
Atzmueller, M 
Heravi, BM 
Gibson, JL 
de Sá, CR 

Essentially, our lives are made of social interactions. These can be recorded through personal gadgets as well as sensors adequately attached to people for research purposes. In particular, such sensors may record real time location of people. This location data can then be used to infer interactions, which may be translated into behavioural patterns. In this paper, we focus on the automatic discovery of exceptional social behaviour from spatio-temporal data. For that, we propose a method for Exceptional Behaviour Discovery (EBD). The proposed method combines Subgroup Discovery and Network Science techniques for finding social behaviour that deviates from the norm. In particular, it transforms movement and demographic data into attributed social interaction networks, and returns descriptive subgroups. We applied the proposed method on two real datasets containing location data from children playing in the school playground. Our results indicate that this is a valid approach which is able to obtain meaningful knowledge from the data.

Subgroup discovery, Network science, Social interactions
Journal Title
EPIA 2019 proceedings, Part II, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference Name
19th EPIA Conference on Artificial Intelligence, EPIA 2019
Journal ISSN
Volume Title
11805 LNAI
Economic and Social Research Council (ES/N006577/1)
This work has been partially supported by the German Research Foundation (DFG) project “MODUS” (under grant AT 88/4-1). Furthermore, the research leading to these results has received funding (JG) from ESRC grant ES/N006577/1. This work was financed by the project Kids First, project number 68639.