3,089 research outputs found
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
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction
With the availability of vast amounts of user visitation history on
location-based social networks (LBSN), the problem of Point-of-Interest (POI)
prediction has been extensively studied. However, much of the research has been
conducted solely on voluntary checkin datasets collected from social apps such
as Foursquare or Yelp. While these data contain rich information about
recreational activities (e.g., restaurants, nightlife, and entertainment),
information about more prosaic aspects of people's lives is sparse. This not
only limits our understanding of users' daily routines, but more importantly
the modeling assumptions developed based on characteristics of recreation-based
data may not be suitable for richer check-in data. In this work, we present an
analysis of education "check-in" data using WiFi access logs collected at
Purdue University. We propose a heterogeneous graph-based method to encode the
correlations between users, POIs, and activities, and then jointly learn
embeddings for the vertices. We evaluate our method compared to previous
state-of-the-art POI prediction methods, and show that the assumptions made by
previous methods significantly degrade performance on our data with dense(r)
activity signals. We also show how our learned embeddings could be used to
identify similar students (e.g., for friend suggestions).Comment: published in KDD'1
Geo Data Science for Tourism
This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations.
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Extracting Personal Behavioral Patterns from Geo-Referenced Tweets
This paper presents an exploratory study of the potential of geo-referenced Twitter data for extracting knowledge about significant personal places, behaviors and potential interests of people. The study was done analysing two months’ worth of tweets from residents of the greater Seattle area
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