Find me if you can: improving geographical prediction with social and spatial proximity

By Lars Backstrom, Eric Sun, Cameron A. Marlow. 2010

780 citations recorded by the source corpus.

Subfields: Information Systems, Statistical and Nonlinear Physics

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In Statistical and Nonlinear Physics

Citations in the corpus

Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, methodology): “05 as empirically determined according to [2].”
A generalized taxonomy of explanations styles for traditional and social recommender systems (2012, background): “Thirdly, we study the impact of social graph in contemporary social recommender systems and their explanation styles, i.e. Facebook Connect recommendations with HuffPost, Netflix etc. and geo-social recommendations (Backstrom et al. 2010; Zheng et al. 2010).”
Center of Attention: How Facebook Users Allocate Attention across Friends (2011, methodology): “Finally , our measure is related to other quantitative trade-offs between focus and dispersion in an individual's personal network, such as the geographic spread of one's friends and the searchability of social networks (Kleinberg 2006; Backstrom, Sun, and Marlow 2010).”
Four degrees of separation (2011, result): “Indeed, we see in this study, as well as other studies of Facebook [1] that, while the world is connected enough for short paths to exist between most nodes, there is a high degree of locality induced by various externalities, geography chief amongst them, all reminiscent of the model proposed in [1”
Recommendations in location-based social networks: a survey (2015, background): “[6] observe that at medium to long-range distances, the probability of friendship is roughly proportional to the inverse of the distance.”
What to do about bad language on the internet (2013, background): “The fact many such neol-ogisms are closely circumscribed in geography and demographics may reﬂect diffusion through social networks that are assortative on exactly these dimensions (Backstrom et al., 2010; Thelwall, 2009).”
Geolocation Prediction in Social Media Data by Finding Location Indicative Words (2012, background): “3 There has been a limited amount of work on geolocation prediction based on social network analysis (Backstrom et al., 2010), but social networks are dynamic and the data is often hard to obtain.”
#Earthquake: Twitter as a Distributed Sensor System (2013, methodology): “Comparable work has been performed on predicting the location of Flickr users through an analysis of their contributions' content (Popescu and Grefenstete 2010), and of Facebook users through an analysis of their social network (Backstrom et al. 2010).”
Exploiting place features in link prediction on location-based social networks (2011, background): “An approach to link prediction is to consider the geographic distance between two users, since geographic proximity is related to higher chances of social connection [16, 2, 19, 4].”
The Effect of Population and "Structural" Biases on Social Media-based Algorithms: A Case Study in Geolocation Inference Across the Urban-Rural Spectrum (2017, background): “, [24]), meaning that connected users are likely close geographically [3].”
You are where you tweet: a content-based approach to geo-locating twitter users (2010, background): “In addition, Twitter began supporting per-tweet geo-tagging in August 2009.”
Spatial Networks (2010, background): “Clustering seems also to be large as shown in [21, 205].”
Socio-Spatial Properties of Online Location-Based Social Networks (2011, background): “…consideration when dealing with social networks (Scellato et al. 2010) and on exploiting simple socio-spatial properties to predict user location (Backstrom, Sun, and Marlow 2010), our contributions are different: we shed new light on how these socio-spatial properties arise from social and…”
Diffusion of Lexical Change in Social Media (2012, background): “In principle social media can connect people across the world, but in practice social media connections tend to be quite local [6, 7].”
Understanding user behavior in online social networks: a survey (2013, background): “In [11], the study of user-contributed address and association data from Facebook shows that the addition of social information produces improvement in accuracy of predicting physical location.”
Human mobility: Models and applications (2017, background): “Similarly, the location of a person may be predicted by those of their near contacts [130].”