Social media users share billions of items per year, only a small fraction of which is geotagged. We present a data- driven approach for identifying non-geotagged content items that can be associated with a hyper-local geographic area by modeling the location distributions of hyper-local n-grams that appear in the text. We explore the trade-off between accuracy, precision and coverage of this method. Further, we explore differences across content received from multiple platforms and devices, and show, for example, that content shared via different sources and applications produces significantly different geographic distributions, and that it is best to model and predict location for items according to their source. Our findings show the potential and the bounds of a data-driven approach to geotag short social media texts, and offer implications for all applications that use data-driven approaches to locate content.
Recent work by @cite_3 mapped users noisy check-ins on Foursquare to semantically extract meaningful suggestions from a database of known points of interest. In particular, by aggregating locations from geotagged check-ins, the authors were able to create geographic models for different venues using multi-dimensional Gaussian models. Earlier work from Flickr, Alpha Shapes code.flickr.net 2008 10 30 the-shape-of-alpha , modeled information available from geotagged images on Flickr to create geographic models for places like neighborhoods, towns, etc. Finally, @cite_23 not only explored point-of-interest mentions on Twitter but also connected them to the relative temporal values of the visits.
@cite_23 @cite_3
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Twitter is a popular platform for sharing activities, plans, and opinions. Through tweets, users often reveal their location information and short term visiting plans. In this paper, we are interested in extracting fine-grained locations mentioned in tweets with temporal awareness. More specifically, we like to extract each point-of-interest (POI) mention in a tweet and predict whether the user has visited, is currently at, or will soon visit this POI. Our proposed solution, named PETAR, consists of two main components: a POI inventory and a time-aware POI tagger. The POI inventory is built by exploiting the crowd wisdom of Foursquare community. It contains not only the formal names of POIs but also the informal abbreviations. The POI tagger, based on Conditional Random Field (CRF) model, is designed to simultaneously identify the POIs and resolve their associated temporal awareness. In our experiments, we investigated four types of features (i.e., lexical, grammatical, geographical, and BILOU schema features) for time-aware POI extraction. With the four types of features, PETAR achieves promising extraction accuracy and outperforms all baseline methods. In this article we consider the problem of mapping a noisy estimate of a user's current location to a semantically meaningful point of interest, such as a home, restaurant, or store. Despite the poor accuracy of GPS on current mobile devices and the relatively high density of places in urban areas, it is possible to predict a user's location with considerable precision by explicitly modeling both places and users and by combining a variety of signals about a user's current context. Places are often simply modeled as a single latitude and longitude when in fact they are complex entities existing in both space and time and shaped by the millions of people that interact with them. Similarly, models of users reveal complex but predictable patterns of mobility that can be exploited for this task. We propose a novel spatial search algorithm that infers a user's location by combining aggregate signals mined from billions of foursquare check-ins with real-time contextual information. We evaluate a variety of techniques and demonstrate that machine learning algorithms for ranking and spatiotemporal models of places and users offer significant improvement over common methods for location search based on distance and popularity.