Mining Exceptional Social Behaviour

Abstract

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.

“Want to come play with me?” Outlier subgroup discovery on spatio‐temporal interactions

“…We focus on the study of subgroup discovery methods and metrics of social networks analysis and outlier detection. Part of this work extends the work proposed in (Atzmueller, 2018;C. C. Jorge et al, 2019) which combined Subgroup Discovery with social interaction networks for detecting exceptional behaviour, applying Compositional Subgroup Discovery on complex interaction networks.…”

“…In this paper, we substantially extend the work on Exceptional Behaviour Discovery (C. C. Jorge et al, 2019), with the specific target of outlier subgroup discovery on spatio-temporal (social) interactions, where we also consider the descriptive characteristics of the involved actors. The goal of this paper is thus to detect and extract characteristics of exceptional behaviour in datasets containing both movement and demographic characteristics.…”

Indoor Location Data for Tracking Human Behaviours: A Scoping Review

“…Classification approaches were found in 11 studies of Consumer Behaviours (e.g., classifying shoppers as impulse or scheduled shoppers) and 3 studies of Operation Efficiency studies (e.g., classifying behaviours as risky/unsafe or safe). Other objectives included (1) a characterization of a population based on their location or movement, e.g., [ 24 , 93 ]; (2) correlation of an environmental or external context-based variable [ 53 , 83 , 86 ]; (3) support of an observational measure or standard [ 23 ]; and (4) development a system that predicts future events or behaviours [ 63 , 69 ]. A notable sub-group (11 studies) of classification-type objectives within the health status monitoring studies was the classification of groups based on risk of specific health deficits, such as cognitive impairment or dementia.…”

“…The authors of [ 83 ] used the proximity of two museum visitors in a group and measured audio levels to estimate social engagement between groups using or not using mobile device guides. The two papers studying developmental behaviours used observations made by teaching staff to provide labels for RTLS data analysis [ 93 , 94 ]. The advanced methods in network science found in [ 93 ] were more complex in algorithms and analyses than any studies observing RTLS for health monitoring.…”

“…The two papers studying developmental behaviours used observations made by teaching staff to provide labels for RTLS data analysis [ 93 , 94 ]. The advanced methods in network science found in [ 93 ] were more complex in algorithms and analyses than any studies observing RTLS for health monitoring. For example, one group [ 47 ] recorded the proximity of participants to correct the accuracy of their location detection system, one [ 78 ] recorded the number and density people in one area to measure customer flow but not interaction, and one [ 22 ] measured proximity with respect to moveable objects and not other participants.…”