Community elicitation from co-occurrence of activities

Abstract

A massive amount of data is continuously generated by the activities that people hold on online platforms, mobile systems and in general using and interacting with digital systems. Big data, not directly related to social networks, are generated in large quantities from systems that are not necessarily social systems. In fact, in the information society a whole lot of activities take place on systems that were not developed to support explicit social interactions between users.In this thesis, starting from the observation of users activities within a system, we want to investigate if it is possible to characterise the existence of social relationships among them. As people interact, individually or in groups, we want to elicit their social communities from the temporal and spatial co-occurrence of their activities.The key assumption of this work is that we suppose that there are multiple, parallel, hidden communication channels and social networks where social interactions take place among users and which determine the observed emergent phenomenon of actions co-occurrences.The main original contribution of this thesis is the proposal of innovative methodologies for users community discovery from implicit user-system interactions and their experimental evaluation. The History Based Analysis approach is a novel approach we have introduced, that exploits the similarity of users' activity histories to discover the hidden social communities. To better characterise the histories binary correlation measures we have introduced and experimented original entropy i amplification factors that take in account system wide distribution of activities at a given time to contextualise the user activity similarities. The other relevant introduced approach, the Session Based method, uses graph based representation of concurrent users' sessions to elicit the hidden social communities. Both proposed approaches have been validated using a real world dataset containing the activity logs of students using a virtual learning environment platform.A remarkable result of our work has been to confirm that co-occurrence of people activities is an emerging epiphenomenon of hidden, implicit information exchanges through side channel communications. Therefore the observation of co-occurrence of events can be used to elicit social relationships. Interesting extension of this work include the analysis of real world co-occurrences, like in the case of people, personal vehicles or other personal objects occurring in the same physical place at the same time, and in general wherever it co-occurrence can be seen as an emerging epiphenomenon of people's relationships and information exchange.Potential applications of this thesis work can fall in various areas such as business, marketing, public administration, including intelligence and military sectors.Experimental evaluation of the introduced methodologies through tests held in the domain of eLearning demonstrated the effectiveness of our proposed approaches in retrieving hidden social communities.

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