Human behavior prediction is critical in understanding and addressing large scale health and social issues in online communities. Specifically, predicting when in the future a user will engage in a behavior as opposed to whether a user will behave at a particular time is a less studied subproblem of behavior prediction. Further lacking is exploration of how social context affects personal behavior and the exploitation of network structure information in behavior and time prediction. To address these problems we propose a novel semi-supervised deep learning model for prediction of return time to personal behavior. A carefully designed objective function ensures the model learns good social context embeddings and historical behavior embeddings in order to capture the effects of social influence on personal behavior. Our model is validated on a unique health social network dataset by predicting when users will engage in physical activities. We show our model outperforms relevant time prediction baselines.

Human behavior prediction is critical to studying how healthy behavior can spread through a social network. In this work we present a novel user representation based human behavior prediction model, the User Representation-based Socialized Gaussian Process model (UrSGP). First, we present the Deep Interaction Representation Learning (Deep Interaction) model for learning latent representations of interaction social networks in which each user is characterized by a set of attributes. In particular, we consider social interaction factors and user attribute factors to build a bimodal, fixed representation of each user in the network. Our model aims to capture the evolution of social interactions and user attributes and learn the hidden correlations between them. We then use our latent features for human behavior prediction via the UrSGP model. An empirical experiment conducted on a real health social network demonstrates that our model outperforms baseline approaches for human behavior prediction.

Modeling physical activity propagation, such as activity level and intensity, is a key to preventing obesity from cascading through communities, and to helping spread wellness and healthy behavior in a social network. However, there have not been enough scientific and quantitative studies to elucidate how social communication may deliver physical activity interventions. In this work, we introduce a novel model named T opic-aware C ommunity-level P hysical Activity Propagation with T emporal Dynamics (TCPT) to analyze physical activity propagation and social influence at different granularities (i.e., individual level and community level). Given a social network, the TCPT model first integrates the correlations between the content of social communication, social influences, and temporal dynamics. Then, a hierarchical approach is utilized to detect a set of communities and their reciprocal influence strength of physical activities. The experimental evaluation shows not only the effectiveness of our approach but also the correlation of the detected communities with various health outcome measures. Our promising results pave a way for knowledge discovery in health social networks.

Modeling and predicting human behaviors, such as the activity level and intensity, is the key to prevent the cascades of obesity, and help spread wellness and healthy behavior in a social network. The user diversity, dynamic behaviors, and hidden social influences make the problem more challenging. In this work, we propose a deep learning model named Social Restricted Boltzmann Machine (SRBM) for human behavior modeling and prediction in health social networks. In the proposed SRBM model, we naturally incorporate self-motivation, implicit and explicit social influences, and environmental events together into three layers which are historical, visible, and hidden layers. The interactions among these behavior determinants are naturally simulated through parameters connecting these layers together. The contrastive divergence and back-propagation algorithms are employed for training the model. A comprehensive experiment on real and synthetic data has shown the great effectiveness of our deep learning model compared with conventional methods.

Modeling physical activity propagation, such as the activity level and intensity, is the key to prevent the cascades of obesity, and help spread wellness and healthy behavior in a social network. However, there has been lacking of scientific and quantitative study to elucidate how social communication may deliver physical activity interventions. In this work we introduce a Community-level Physical Activity Propagation (CPP) model to analyze physical activity propagation and social influence at different granularities (i.e., individual level and community level). CPP is a novel model which is inspired by the well-known Independent Cascade and Community-level Social Influence models. Given a social network, we utilize a hierarchical approach to detect a set of communities and their reciprocal influence strength of physical activities. CPP provides a powerful tool to discover, summarize, and investigate influence patterns of physical activities in a health social network. The detail experimental evaluation shows not only the effectiveness of our approach but also the correlation of the detected communities with various health outcome measures (i.e., both existing ones and our novel measure, named Wellness score, which is a combination of lifestyle parameters, biometrics, and biomarkers). Our promising results potentially pave a way for knowledge discovery in health social networks.

Modeling and predicting human behaviors, such as the activity level and intensity, is the key to prevent the cascades of obesity, and help spread wellness and healthy behavior in a social network. In this work, we propose a Socialized Gaussian Process (SGP) for socialized human behavior modeling. In the proposed SGP model, we naturally incorporates human's personal behavior factor and social correlation factor into a unified model, where basic Gaussian Process model is leveraged to capture individual's personal behavior pattern. Furthermore, we extend the Gaussian Process Model to socialized Gaussian Process (SGP) which aims to capture social correlation phenomena in the social network. The detailed experimental evaluation has shown the SGP model achieves the best prediction accuracy compared with other baseline methods.