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Accession Number ADA581446
Title Modeling Human Behavior at a Large Scale.
Publication Date 2012
Media Count 197p
Personal Author A. Sadilek
Abstract Until recently, complex phenomena such as human behavior and disease epidemics have been modeled primarily at an aggregate level. Detailed studies have been limited to small domains encompassing only a few subjects, as scaling the methods involved poses considerable challenges in terms of cost, human effort required, computational bottlenecks, and data sources available. With the surge of online social media and sensor networks, the abundance of interesting and publicly accessible data is beginning to increase. However, we also need the ability to reason about it efficiently. The underlying theme of this thesis is the unification and data mining of diverse, noisy, and incomplete sensory data over large numbers of individuals. We show that the mined patterns can be leveraged in predictive models of human behavior and other phenomena at a large scale. We find that raw sensory data linked with the content of users' online communication, the explicit as well as the implicit online social interactions, and interpersonal relationships are rich information sources upon which strong machine learning models can be built. Example domains where such models apply include understanding human activities, predicting people's location and social ties from their online behavior, and predicting the emergence of global epidemics from day-to-day interpersonal interactions.
Keywords Data mining
Epidemiology
Global positioning system
Information processing
Interactions
Interpersonal relations
Location based reasoning
Markov logic networks
Position(Location)
Predictive models
Sbir reports
Sbir(Small business innovation research)
Scalable artificial intelligence models
Smart phones
Social media
Social network analysis
Text processing
Theses
Twitter


 
Source Agency Non Paid ADAS
NTIS Subject Category 92C - Social Concerns
57E - Clinical Medicine
57U - Public Health & Industrial Medicine
72F - Statistical Analysis
Corporate Author Rochester Univ., NY. Dept. of Computer Science.
Document Type Thesis
Title Note Doctoral thesis.
NTIS Issue Number 1325
Contract Number W911NF-08-1-0242

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