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.
A related set of studies used information about geographic regions in geotagged social media to extract information and characterize geographic areas @cite_8 @cite_16 @cite_10 @cite_19 @cite_6 @cite_29 .
@cite_8 @cite_29 @cite_6 @cite_19 @cite_16 @cite_10
2142889507 2127860643 2021278340 2950066308
The rapid growth of geotagged social media raises new computational possibilities for investigating geographic linguistic variation. In this paper, we present a multi-level generative model that reasons jointly about latent topics and geographical regions. High-level topics such as "sports" or "entertainment" are rendered differently in each geographic region, revealing topic-specific regional distinctions. Applied to a new dataset of geotagged microblogs, our model recovers coherent topics and their regional variants, while identifying geographic areas of linguistic consistency. The model also enables prediction of an author's geographic location from raw text, outperforming both text regression and supervised topic models. This paper studies the problem of discovering and comparing geographical topics from GPS-associated documents. GPS-associated documents become popular with the pervasiveness of location-acquisition technologies. For example, in Flickr, the geo-tagged photos are associated with tags and GPS locations. In Twitter, the locations of the tweets can be identified by the GPS locations from smart phones. Many interesting concepts, including cultures, scenes, and product sales, correspond to specialized geographical distributions. In this paper, we are interested in two questions: (1) how to discover different topics of interests that are coherent in geographical regions? (2) how to compare several topics across different geographical locations? To answer these questions, this paper proposes and compares three ways of modeling geographical topics: location-driven model, text-driven model, and a novel joint model called LGTA (Latent Geographical Topic Analysis) that combines location and text. To make a fair comparison, we collect several representative datasets from Flickr website including Landscape, Activity, Manhattan, National park, Festival, Car, and Food. The results show that the first two methods work in some datasets but fail in others. LGTA works well in all these datasets at not only finding regions of interests but also providing effective comparisons of the topics across different locations. The results confirm our hypothesis that the geographical distributions can help modeling topics, while topics provide important cues to group different geographical regions. Geographically annotated social media is extremely valuable for modern information retrieval. However, when researchers can only access publicly-visible data, one quickly finds that social media users rarely publish location information. In this work, we provide a method which can geolocate the overwhelming majority of active Twitter users, independent of their location sharing preferences, using only publicly-visible Twitter data. Our method infers an unknown user's location by examining their friend's locations. We frame the geotagging problem as an optimization over a social network with a total variation-based objective and provide a scalable and distributed algorithm for its solution. Furthermore, we show how a robust estimate of the geographic dispersion of each user's ego network can be used as a per-user accuracy measure which is effective at removing outlying errors. Leave-many-out evaluation shows that our method is able to infer location for 101, 846, 236 Twitter users at a median error of 6.38 km, allowing us to geotag over 80 of public tweets. Associating geo-coordinates with the content of social media posts can enhance many existing applications and services and enable a host of new ones. Unfortunately, a majority of social media posts are not tagged with geo-coordinates. Even when location data is available, it may be inaccurate, very broad or sometimes fictitious. Contemporary location estimation approaches based on analyzing the content of these posts can identify only broad areas such as a city, which limits their usefulness. To address these shortcomings, this paper proposes a methodology to narrowly estimate the geo-coordinates of social media posts with high accuracy. The methodology relies solely on the content of these posts and prior knowledge of the wide geographical region from where the posts originate. An ensemble of language models, which are smoothed over non-overlapping sub-regions of a wider region, lie at the heart of the methodology. Experimental evaluation using a corpus of over half a million tweets from New York City shows that the approach, on an average, estimates locations of tweets to within just 2.15km of their actual positions.