Sanghack Lee

Teens are from mars, adults are from venus: analyzing and predicting age groups with behavioral characteristics in instagram

We present behavioral characteristics of teens and adults in Instagram and prediction of them from their behaviors. Based on two independently created datasets from user profiles and tags, we identify teens and adults, and carry out comparative analyses on their online behaviors. Our study reveals: (1) significant behavioral differences between two age groups; (2) the empirical evidence of classifying teens and adults with up to 82% accuracy, using traditional predictive models, while two baseline methods achieve 68% at best; and (3) the robustness of our models by achieving 76%---81% when tested against an independent dataset obtained without using user profiles or tags. Our datasets are available at: https://goo.gl/LqTYNv

Learning Classifiers from Distributional Data

Many big data applications give rise to distributional data wherein objects or individuals are naturally represented as K-tuples of bags of feature values where feature values in each bag are sampled from a feature and object specific distribution. We formulate and solve the problem of learning classifiers from distributional data. We consider three classes of methods for learning distributional classifiers: (i) those that rely on aggregation to encode distributional data into tuples of attribute values, i.e., instances that can be handled by traditional supervised machine learning algorithms, (ii) those that are based on generative models of distributional data, and (iii) the discriminative counterparts of the generative models considered in (ii) above. We compare the performance of the different algorithms on real-world as well as synthetic distributional data sets. The results of our experiments demonstrate that classifiers that take advantage of the information available in the distributional instance representation outperform or match the performance of those that fail to fully exploit such information.