4,268 research outputs found
Spatio-semantic user profiles in location-based social networks
Knowledge of users’ visits to places is one of the keys to understanding their interest in places. User-contributed annotations of place, the types of places they visit, and the activities they carry out, add a layer of important semantics that, if considered, can result in more refined representations of user profiles. In this paper, semantic information is summarised as tags for places and a folksonomy data model is used to represent spatial and semantic relationships between users, places, and tags. The model allows simple co-occurrence methods and similarity measures to be applied to build different views of personalised user profiles. Basic profiles capture direct user interactions, while enriched profiles offer an extended view of users’ association with places and tags that take into account relationships in the folksonomy. The main contributions of this work are the proposal of a uniform approach to the creation of user profiles on the Social Web that integrates both the spatial and semantic components of user-provided information, and the demonstration of the effectiveness of this approach with realistic datasets
Hoodsquare: Modeling and Recommending Neighborhoods in Location-based Social Networks
Information garnered from activity on location-based social networks can be
harnessed to characterize urban spaces and organize them into neighborhoods. In
this work, we adopt a data-driven approach to the identification and modeling
of urban neighborhoods using location-based social networks. We represent
geographic points in the city using spatio-temporal information about
Foursquare user check-ins and semantic information about places, with the goal
of developing features to input into a novel neighborhood detection algorithm.
The algorithm first employs a similarity metric that assesses the homogeneity
of a geographic area, and then with a simple mechanism of geographic
navigation, it detects the boundaries of a city's neighborhoods. The models and
algorithms devised are subsequently integrated into a publicly available,
map-based tool named Hoodsquare that allows users to explore activities and
neighborhoods in cities around the world.
Finally, we evaluate Hoodsquare in the context of a recommendation
application where user profiles are matched to urban neighborhoods. By
comparing with a number of baselines, we demonstrate how Hoodsquare can be used
to accurately predict the home neighborhood of Twitter users. We also show that
we are able to suggest neighborhoods geographically constrained in size, a
desirable property in mobile recommendation scenarios for which geographical
precision is key.Comment: ASE/IEEE SocialCom 201
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
Where you go is who you are -- A study on machine learning based semantic privacy attacks
Concerns about data privacy are omnipresent, given the increasing usage of
digital applications and their underlying business model that includes selling
user data. Location data is particularly sensitive since they allow us to infer
activity patterns and interests of users, e.g., by categorizing visited
locations based on nearby points of interest (POI). On top of that, machine
learning methods provide new powerful tools to interpret big data. In light of
these considerations, we raise the following question: What is the actual risk
that realistic, machine learning based privacy attacks can obtain meaningful
semantic information from raw location data, subject to inaccuracies in the
data? In response, we present a systematic analysis of two attack scenarios,
namely location categorization and user profiling. Experiments on the
Foursquare dataset and tracking data demonstrate the potential for abuse of
high-quality spatial information, leading to a significant privacy loss even
with location inaccuracy of up to 200m. With location obfuscation of more than
1 km, spatial information hardly adds any value, but a high privacy risk solely
from temporal information remains. The availability of public context data such
as POIs plays a key role in inference based on spatial information. Our
findings point out the risks of ever-growing databases of tracking data and
spatial context data, which policymakers should consider for privacy
regulations, and which could guide individuals in their personal location
protection measures
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