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An Empirical Evaluation of Social Influence Metrics


Abstract Predicting when an individual will adopt a new behavior is an important problem in application domains such as marketing and public health. This thesis examines the performance of a wide variety of social network based measurements proposed in the literature - which have not been previously compared directly. This research studies the probability of an individual becoming influenced based on measurements derived from neighborhood (i.e. number of influencers, personal network exposure), structural diversity, locality, temporal measures, cascade measures, and metadata. It also examines the ability to predict influence based on choice of the classifier and how the ratio of positive to negative samples in both training and testing affect p... (more)
Created Date 2016
Contributor Nanda Kumar, Nikhil (Author) / Shakarian, Paulo (Advisor) / Sen, Arunabha (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Subject Computer science / social influence
Type Masters Thesis
Extent 41 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis