85 research outputs found
Efficient classification of billions of points into complex geographic regions using hierarchical triangular mesh
We present a case study about the spatial indexing and regional
classification of billions of geographic coordinates from geo-tagged social
network data using Hierarchical Triangular Mesh (HTM) implemented for Microsoft
SQL Server. Due to the lack of certain features of the HTM library, we use it
in conjunction with the GIS functions of SQL Server to significantly increase
the efficiency of pre-filtering of spatial filter and join queries. For
example, we implemented a new algorithm to compute the HTM tessellation of
complex geographic regions and precomputed the intersections of HTM triangles
and geographic regions for faster false-positive filtering. With full control
over the index structure, HTM-based pre-filtering of simple containment
searches outperforms SQL Server spatial indices by a factor of ten and
HTM-based spatial joins run about a hundred times faster.Comment: appears in Proceedings of the 26th International Conference on
Scientific and Statistical Database Management (2014
Video Pandemics: Worldwide Viral Spreading of Psy's Gangnam Style Video
Viral videos can reach global penetration traveling through international
channels of communication similarly to real diseases starting from a
well-localized source. In past centuries, disease fronts propagated in a
concentric spatial fashion from the the source of the outbreak via the short
range human contact network. The emergence of long-distance air-travel changed
these ancient patterns. However, recently, Brockmann and Helbing have shown
that concentric propagation waves can be reinstated if propagation time and
distance is measured in the flight-time and travel volume weighted underlying
air-travel network. Here, we adopt this method for the analysis of viral meme
propagation in Twitter messages, and define a similar weighted network distance
in the communication network connecting countries and states of the World. We
recover a wave-like behavior on average and assess the randomizing effect of
non-locality of spreading. We show that similar result can be recovered from
Google Trends data as well.Comment: 10 page
Scaling in Words on Twitter
Scaling properties of language are a useful tool for understanding generative
processes in texts. We investigate the scaling relations in citywise Twitter
corpora coming from the Metropolitan and Micropolitan Statistical Areas of the
United States. We observe a slightly superlinear urban scaling with the city
population for the total volume of the tweets and words created in a city. We
then find that a certain core vocabulary follows the scaling relationship of
that of the bulk text, but most words are sensitive to city size, exhibiting a
super- or a sublinear urban scaling. For both regimes we can offer a plausible
explanation based on the meaning of the words. We also show that the parameters
for Zipf's law and Heaps law differ on Twitter from that of other texts, and
that the exponent of Zipf's law changes with city size
Location-aware online learning for top-k recommendation
We address the problem of recommending highly volatile items for users, both with potentially ambiguous location that may change in time. The three main ingredients of our method include (1) using online machine learning for the highly volatile items; (2) learning the personalized importance of hierarchical geolocation (for example, town, region, country, continent); finally (3) modeling temporal relevance by counting recent items with an exponential decay in recency.For (1), we consider a time-aware setting, where evaluation is cumbersome by traditional measures since we have different top recommendations at different times. We describe a time-aware framework based on individual item discounted gain. For (2), we observe that trends and geolocation turns out to be more important than personalized user preferences: user-item and content-item matrix factorization improves in combination with our geo-trend learning methods, but in itself, they are greatly inferior to our location based models. In fact, since our best performing methods are based on spatiotemporal data, they are applicable in the user cold start setting as well and perform even better than content based cold start methods. Finally for (3), we estimate the probability that the item will be viewed by its previous views to obtain a powerful model that combines item popularity and recency.To generate realistic data for measuring our new methods, we rely on Twitter messages with known GPS location and consider hashtags as items that we recommend the users to be included in their next message. © 2016 Elsevier B.V
Race, Religion and the City: Twitter Word Frequency Patterns Reveal Dominant Demographic Dimensions in the United States
Recently, numerous approaches have emerged in the social sciences to exploit
the opportunities made possible by the vast amounts of data generated by online
social networks (OSNs). Having access to information about users on such a
scale opens up a range of possibilities, all without the limitations associated
with often slow and expensive paper-based polls. A question that remains to be
satisfactorily addressed, however, is how demography is represented in the OSN
content? Here, we study language use in the US using a corpus of text compiled
from over half a billion geo-tagged messages from the online microblogging
platform Twitter. Our intention is to reveal the most important spatial
patterns in language use in an unsupervised manner and relate them to
demographics. Our approach is based on Latent Semantic Analysis (LSA) augmented
with the Robust Principal Component Analysis (RPCA) methodology. We find
spatially correlated patterns that can be interpreted based on the words
associated with them. The main language features can be related to slang use,
urbanization, travel, religion and ethnicity, the patterns of which are shown
to correlate plausibly with traditional census data. Our findings thus validate
the concept of demography being represented in OSN language use and show that
the traits observed are inherently present in the word frequencies without any
previous assumptions about the dataset. Thus, they could form the basis of
further research focusing on the evaluation of demographic data estimation from
other big data sources, or on the dynamical processes that result in the
patterns found here
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