Eric Bigelow

Bio: Eric Bigelow is an academic researcher from University of Rochester. The author has contributed to research in topics: Metropolitan area & Social group. The author has an hindex of 3, co-authored 5 publications receiving 48 citations.

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

Abstract: Lifestyles are a valuable model for understanding individuals’ physical and mental lives, comparing social groups, and making recommendations for improving people's 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.

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

Abstract: Lifestyles are a valuable model for understanding individuals' physical and mental lives, comparing social groups, and making recommendations for improving people's 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.

On the Need for Imagistic Modeling in Story Understanding

Abstract: There is ample evidence that human understanding of ordinary language relies in part on a rich capacity for imagistic mental modeling. We argue that genuine language understanding in machines will similarly require an imagistic modeling capacity enabling fast construction of instances of prototypical physical situations and events, whose participants are drawn from a wide variety of entity types, including animate agents. By allowing fast evaluation of predicates such as ‘can-see’, ‘under’, and ‘inside’, these model instances support coherent text interpretation. Imagistic modeling is thus a crucial – and not very broadly appreciated – aspect of the long-standing knowledge acquisition bottleneck in AI. We will illustrate how the need for imagistic modeling arises even in the simplest first-reader stories for children, and provide an initial feasibility study to indicate what the architecture of a system combining symbolic with imagistic understanding might look like.

A Large Dataset of Generalization Patterns in the Number Game

Abstract: We present a dataset with 272,700 two-alternative forced choice responses in a simple numerical task modeled after Tenenbaum’s “number game” experiment [6]. Subjects were shown a set (e.g. {16, 12}) and asked what other numbers were likely to belong to that set (e.g. 1, 5, 2, 98). Their generalization patterns reflect both rule-like (e.g. ‘even numbers,’ ‘powers of two’) and distance-based (e.g. ‘numbers near 50’) generalization. This dataset is available for further analysis of these simple and intuitive inferences, developing of hands-on modeling instruction, and attempts to understand how probability and rules interact in human cognition.

Learning and the language of thought

Abstract: Logic and probability are key themes of cognitive science that have long had an uneasy coexistence. I will describe the Probabilistic Language of Thought approach that brings them together into compositional representations with probabilistic meaning - formalized as stochastic lambda calculus. I will describe how this general framework is realized in the probabilistic programming language Church.

A review of human mobility research based on big data and its implication for smart city development

Abstract: Along with the increase of big data and the advancement of technologies, comprehensive data-driven knowledge of urban systems is becoming more attainable, yet the connection between big-data research and its application e.g., in smart city development, is not clearly articulated. Focusing on Human Mobility, one of the most frequently investigated applications of big data analytics, a framework for linking international academic research and city-level management policy was established and applied to the case of Hong Kong. Literature regarding human mobility research using big data are reviewed. These studies contribute to (1) discovering the spatial-temporal phenomenon, (2) identifying the difference in human behaviour or spatial attributes, (3) explaining the dynamic of mobility, and (4) applying to city management. Then, the application of the research to smart city development are scrutinised based on email queries to various governmental departments in Hong Kong. The identified challenges include data isolation, data unavailability, gaming between costs and quality of data, limited knowledge derived from rich data, as well as estrangement between public and private sectors. With further improvement in the practical value of data analytics and the utilization of data sourced from multiple sectors, paths to achieve smarter cities from policymaking perspectives are highlighted.

Understanding Cross-Site Linking in Online Social Networks

Abstract: As a result of the blooming of online social networks (OSNs), a user often holds accounts on multiple sites. In this article, we study the emerging “cross-site linking” function available on mainstream OSN services including Foursquare, Quora, and Pinterest. We first conduct a data-driven analysis on crawled profiles and social connections of all 61.39 million Foursquare users to obtain a thorough understanding of this function. Our analysis has shown that the cross-site linking function is adopted by 57.10% of all Foursquare users, and the users who have enabled this function are more active than others. We also find that the enablement of cross-site linking might lead to privacy risks. Based on cross-site links between Foursquare and external OSN sites, we formulate cross-site information aggregation as a problem that uses cross-site links to stitch together site-local information fields for OSN users. Using large datasets collected from Foursquare, Facebook, and Twitter, we demonstrate the usefulness and the challenges of cross-site information aggregation. In addition to the measurements, we carry out a survey collecting detailed user feedback on cross-site linking. This survey studies why people choose to or not to enable cross-site linking, as well as the motivation and concerns of enabling this function.