Tianran Hu

Tales of Two Cities: Using Social Media to Understand Idiosyncratic Lifestyles in Distinctive Metropolitan Areas

Lifestyles are a valuable model for understanding individuals’ physical and mental lives, comparing social groups, and making recommendations for improving peoples lives. In this paper, we examine and compare lifestyle behaviors of people living in cities of different sizes, utilizing freely available social media data as a large-scale, low-cost alternative to traditional survey methods. We use the Greater New York City area as a representative for large cities, and the Greater Rochester area as a representative for smaller cities in the United States. We employed matrix factor analysis as an unsupervised method to extract salient mobility and work-rest patterns for a large population of users within each metropolitan area. We discovered interesting human behavior patterns at both a larger scale and a finer granularity than is present in previous literature, some of which allow us to quantitatively compare the behaviors of individuals of living in big cities to those living in small cities. We believe that our social media-based approach to lifestyle analysis represents a powerful tool for social computing in the big data age.

Inferring Home Location from User's Photo Collections based on Visual Content and Mobility Patterns

Precise home location detection has been actively studied in the past few years. It is indispensable in the researching fields such as personalized marketing and disease propagation. Since the last few decades, the rapid growth of geotagged multimedia database from online social networks provides a valuable opportunity to predict peoples home location from temporal, spatial and visual cues. Among the massive amount of social media data, one important type of data is the geotagged web images from image-sharing websites. In this paper, we developed a reliable photo classifier based on the Convolutional Neutral Networks to classify photos as either home or non-home. We then proposed a novel approach to home location prediction by fusing together the visual content of web images and the spatiotemporal features of peoples mobility pattern. Using a linear SVM classifier, we showed that the robust fusion of visual and temporal feature achieves significant accuracy improvement over each of the features alone.

Home Location Inference from Sparse and Noisy Data: Models and Applications

Accurate home location is increasingly important for urban computing. Existing methods either rely on continuous (and expensive) GPS data or suffer from poor accuracy. In particular, the sparse and noisy nature of social media data poses serious challenges in pinspointing where people live at scale. We revisit this research topic and infer home location within 100 by 100 meter squares at 70% accuracy for 71% and 76% of active users in New York City and the Bay Area, respectively. We believe this is the first time home location is detected at such a fine granularity using sparse and noisy data. Since people spend a large portion of their time at home, our model enables novel applications that were previously impossible. As a specific example, we focus on modeling peoples health at scale.

Touch Your Heart: A Tone-aware Chatbot for Customer Care on Social Media

Chatbot has become an important solution to rapidly increasing customer care demands on social media in recent years. However, current work on chatbot for customer care ignores a key to impact user experience - tones. In this work, we create a novel tone-aware chatbot that generates toned responses to user requests on social media. We first conduct a formative research, in which the effects of tones are studied. Significant and various influences of different tones on user experience are uncovered in the study. With the knowledge of effects of tones, we design a deep learning based chatbot that takes tone information into account. We train our system on over 1.5 million real customer care conversations collected from Twitter. The evaluation reveals that our tone-aware chatbot generates as appropriate responses to user requests as human agents. More importantly, our chatbot is perceived to be even more empathetic than human agents.

Decode Human Life from Social Media

In this big data era, people leave clues of their life consciously or unconsciously on many social media platforms in various forms. By mining data from social media, researchers can uncover the patterns of human life at both individual and group levels. Social media is one of the major data sources for such studies for mainly two reasons. 1) The huge volume and open access of data on these platforms, and 2) the diversity of data on different platforms, such as multimedia data on Twitter and Facebook, geolocation data on Foursquare and Yelp, as well as career data on Linkedin. In this paper, we introduce our work on studying human life based on social media data, and report the plan for our subsequent studies. Our work is intended to decodes human life from two perspectives. From a linguistic perspective, we study the language patterns of different social groups of people. The learned language patterns can reveal the specific characteristics of these groups, and provide novel angles to understanding people. From a mobility perspective, we extract the mobility patterns of individual person, and groups of people such as residents of certain regions. Using the detected mobility patterns, we mine knowledge of human life including the lifestyles and shopping patterns of cities and regions. We intend to combine these two perspectives in our ongoing work, and introduce a novel framework for study human life.

Inferring restaurant styles by mining crowd sourced photos from user-review websites

When looking for a restaurant online, user uploaded photos often give people an immediate and tangible impression about a restaurant. Due to their informativeness, such user contributed photos are leveraged by restaurant review websites to provide their users an intuitive and effective search experience. In this paper, we present a novel approach to inferring restaurant types or styles (ambiance, dish styles, suitability for different occasions) from user uploaded photos on user-review websites. To that end, we first collect a novel restaurant photo dataset associating the user contributed photos with the restaurant styles from TripAdvior. We then propose a deep multi-instance multi-label learning (MIML) framework to deal with the unique problem setting of the restaurant style classification task. We employ a two-step bootstrap strategy to train a multi-label convolutional neural network (CNN). The multi-label CNN is then used to compute the confidence scores of restaurant styles for all the images associated with a restaurant. The computed confidence scores are further used to train a final binary classifier for each restaurant style tag. Upon training, the styles of a restaurant can be profiled by analyzing restaurant photos with the trained multi-label CNN and SVM models. Experimental evaluation has demonstrated that our crowd sourcing-based approach can effectively infer the restaurant style when there are a sufficient number of user uploaded photos for a given restaurant.