On neighborhood effects in location-based social networks

National Research Foundation (NRF) Singapore under International Research Centre @ Singapore Funding Initiativ

Tweet-SCAN: an event discovery technique for geo-located tweets

Twitter has become one of the most popular Location-based Social Networks (LBSNs) that bridges physical and virtual worlds. Tweets, 140-character-long messages, are aimed to give answer to the What’s happening? question. Occurrences and events in the real life (such as political protests, music concerts, natural disasters or terrorist acts) are usually reported through geo-located tweets by users on site. Uncovering event-related tweets from the rest is a challenging problem that necessarily requires exploiting different tweet features. With that in mind, we propose Tweet-SCAN, a novel event discovery technique based on the popular density-based clustering algorithm called DBSCAN. Tweet-SCAN takes into account four main features from a tweet, namely content, time, location and user to group together event-related tweets. The proposed technique models textual content through a probabilistic topic model called Hierarchical Dirichlet Process and introduces Jensen–Shannon distance for the task of neighborhood identification in the textual dimension. As a matter of fact, we show Tweet-SCAN performance in two real data sets of geo-located tweets posted during Barcelona local festivities in 2014 and 2015, for which some of the events were identified by domain experts beforehand. Through these tagged data sets, we are able to assess Tweet-SCAN capabilities to discover events, justify using a textual component and highlight the effects of several parameters.Peer ReviewedPostprint (author's final draft

Culture and Urban Revitalization: A Harvest Document

Advocates have long argued that the economic benefits of the arts and culture provide a firm rationale for public support. Recent scholarship on the "creative class" and "creative economy" is simply the latest effort to link cultural expression to community prosperity. In contrast, the social benefits of cultural engagement have received relatively little attention, even though -- as we shall see -- they provide a stronger case.We need to avoid a simplistic either-or choice between the economic and social impacts of the arts. People who live in our cities, suburbs, and countryside are simultaneously consumers, workers, residents, citizens, and participants. Culture's role in promoting community capacity and civic engagement is central to its potential for generating vital cultural districts. To separate the economic and the social impacts of the arts makes each more difficult to understand.This document provides an overview of the state-of-the-art literature on culture and urban revitalization. In Part 2, we place the creative sector in contemporary context with a discussion of three social dynamics. The "new urban reality" has restructured our cities by increasing social diversity -- fueled by new residential patterns, the emergence of young adult districts, and immigration; expanding economic inequality; and changing urban form. Shifts in the economic and political environment have changed the structure of the creative sector. Finally, the changing balance of government, nonprofit, and for-profit institutions in social policy development -- the shift to transactional policymaking -- has profound implications for cultural policy and the creative sector broadly defined. These three forces -- the new urban reality, the changing structure of the creative sector, and the emergence of transactional policy-making -- define the context within which culture-based revitalization takes place

Consequences of Content Diversity for Online Public Spaces for Local Communities

While there is significant potential for social technologies to strengthen local communities, creating viable online spaces for them remains difficult. Maintaining a reliable content stream is challenging for local communities with their bounded emphases and limited population of potential contributors. Some systems focus on specific information types (e.g. restaurant, events). Others allow many different information types. This paper reports our findings about the consequences of content diversity from a study of neighborhood-oriented Facebook groups. The findings raise questions about the viability of designs for local online communities that focus narrowly on single topics, goals, and audiences

The Impact of Transit-Oriented Development on Social Capital

This paper focuses on the ability of Transit Oriented Development (TOD) to improve social capital and interactions within a community. The expectation is that TOD has a positive impact on the lifestyle and activities of individuals who reside, work, and frequent these locations, and that this can include increases in social capital. Using data from a survey of transit station locations in New Jersey, the authors examine how proximity to the station and various built environment variables are associated with different measures of social capital, derived from responses to survey questions. These questions inquire about respondents’ perceptions of their neighborhood as a place to live, sense of community, knowing their neighbors, trust, and whether their community is a good place to raise a child. The authors also include a question on volunteering in the community. These questions reflect various domains of social capital as established in the literature. Results generally do not support the hypothesis that social capital is associated with transit station proximity and TOD. Features of the built environment, proxied by population and employment density, are also not associated with increased social capital, and in some cases have a negative association. While there are some limited positive associations with some of the social capital variables, one of the strongest indicators is living in a detached family home

Place of Work and Place of Residence: Informal Hiring Networks and Labor Market Outcomes

We use a novel dataset and research design to empirically detect the effect of social interactions among neighbors on labor market outcomes. Specifically, using Census data that characterize residential and employment locations down to the city block, we examine whether individuals residing in the same block are more likely to work together than those in nearby blocks. We find evidence of significant social interactions operating at the block level: residing on the same versus nearby blocks increases the probability of working together by over 33 percent. The results also indicate that this referral effect is stronger when individuals are similar in sociodemographic characteristics (e.g., both have children of similar ages) and when at least one individual is well attached to the labor market. These findings are robust across various specifications intended to address concerns related to sorting and reverse causation. Further, having determined the characteristics of a pair of individuals that lead to an especially strong referral effect, we provide evidence that the increased availability of neighborhood referrals has a significant impact on a wide range of labor market outcomes including employment and wages.Neighborhood Effects, Job Referrals, Social Interactions, Social Interactions, Social Networks, Labor Supply

Place of Work and Place of Residence: Informal Hiring Networks and Labor Market Outcomes

We use a novel research design to empirically detect the effect of social interactions among neighbors on labor market outcomes. Specifically, using Census data that characterize residential and employment locations down to the city block, we examine whether individuals residing in the same block are more likely to work together than those in nearby blocks. We find evidence of significant social interactions operating at the block level: residing on the same versus nearby blocks increases the probability of working together by over 33 percent. The results also indicate that this referral effect is stronger when individuals are similar in socio-demographic characteristics (e.g., both have children of similar ages) and when at least one individual is well attached to the labor market. These findings are robust across various specifications intended to address concerns related to sorting and reverse causation. Further, having determined the characteristics of a pair of individuals that lead to an especially strong referral effect, we provide evidence that the increased availability of neighborhood referrals has a significant impact on a wide range of labor market outcomes including labor force participation, hours and earnings.

Link creation and profile alignment in the aNobii social network

The present work investigates the structural and dynamical properties of aNobii\footnote{http://www.anobii.com/}, a social bookmarking system designed for readers and book lovers. Users of aNobii provide information about their library, reading interests and geographical location, and they can establish typed social links to other users. Here, we perform an in-depth analysis of the system's social network and its interplay with users' profiles. We describe the relation of geographic and interest-based factors to social linking. Furthermore, we perform a longitudinal analysis to investigate the interplay of profile similarity and link creation in the social network, with a focus on triangle closure. We report a reciprocal causal connection: profile similarity of users drives the subsequent closure in the social network and, reciprocally, closure in the social network induces subsequent profile alignment. Access to the dynamics of the social network also allows us to measure quantitative indicators of preferential linking.Comment: http://www.iisocialcom.org/conference/socialcom2010

Identifying Individual and Group Effects in the Presence of Sorting: A Neighborhood Effects Application

Researchers have long recognized that the non-random sorting of individuals into groups generates correlation between individual and group attributes that is likely to bias naïve estimates of both individual and group effects. This paper proposes a non-parametric strategy for identifying these effects in a model that allows for both individual and group unobservables, applying this strategy to the estimation of neighborhood effects on labor market outcomes. The first part of this strategy is guided by a robust feature of the equilibrium in vertical sorting models - a monotonic relationship between neighborhood housing prices and neighborhood quality. This implies that under certain conditions a non-parametric function of neighborhood housing prices serves as a suitable control function for the neighborhood unobservable in the labor market outcome regression. This control function transforms the problem to a model with one unobservable so that traditional instrumental variables solutions may be applied. In our application, we instrument for each individual’s observed neighborhood attributes with the average neighborhood attributes of a set of observationally identical individuals. The neighborhood effects model is estimated using confidential microdata from the 1990 Decennial Census for the Boston MSA. The results imply that the direct effects of geographic proximity to jobs, neighborhood poverty rates, and average neighborhood education are substantially larger than the conditional correlations identified using OLS, although the net effect of neighborhood quality on labor market outcomes remains small. These findings are robust across a wide variety of specifications and robustness checks.