Determining the geographic focus of digital media is an essential first step for modern geographic information retrieval. However, publicly-visible location annotations are remarkably sparse in online data. In this work, we demonstrate a method which infers the geographic focus of an online document by examining the locations of Twitter users who share links to the document. We apply our geotagging technique to multiple datasets built from different content: manually-annotated news articles, GDELT, YouTube, Flickr, Twitter, and Tumblr.
@cite_14: We describe Web-a-Where, a system for associating geography with Web pages. Web-a-Where locates mentions of places and determines the place each name refers to. In addition, it assigns to each page a geographic focus --- a locality that the page discusses as a whole. The tagging process is simple and fast, aimed to be applied to large collections of Web pages and to facilitate a variety of location-based applications and data analyses.Geotagging involves arbitrating two types of ambiguities: geo non-geo and geo geo. A geo non-geo ambiguity occurs when a place name also has a non-geographic meaning, such as a person name (e.g., Berlin) or a common word (Turkey). Geo geo ambiguity arises when distinct places have the same name, as in London, England vs. London, Ontario.An implementation of the tagger within the framework of the WebFountain data mining system is described, and evaluated on several corpora of real Web pages. Precision of up to 82 on individual geotags is achieved. We also evaluate the relative contribution of various heuristics the tagger employs, and evaluate the focus-finding algorithm using a corpus pretagged with localities, showing that as many as 91 of the foci reported are correct up to the country level.
@cite_22: Mobile devices are increasingly the primary way people access information on the Web. More and more users are carrying small computing devices with them wherever they go and can access the Internet regardless of their location. Whereas prior to the wide adoption of mobile devices, a user’s online life and offline life were partitioned by time and location, now there is less of a division. Users can carry their online friends with them and maintain a constant contact in the form of status updates and uploaded pictures and video, in a running social commentary that blurs the distinction between online and offline interactions. This has lead to a proliferation of data that connects a user’s online social network with their offline activities. As a result, the data they create provides a solid link between users, what they are doing offline, and what they are contributing online. The question is how to harness this content.
@cite_27: Web pages may be organized, indexed, searched, and navigated along several di erent feature dimensions. We investigate di erent approaches to discovering geographic context for web pages, and describe a navigational tool for browsing web resources by geographic proximity.
@cite_15: Named entity tagging comprises the sub-tasks of identifying a text span and classifying it, but this view ignores the relationship between the entities and the world. Spatial and temporal entities ground events in space-time, and this relationship is vital for applications such as question answering and event tracking. There is much recent work regarding the temporal dimension [13, 10], but no extensive study of the spatial dimension. I propose to investigate how spatial named entities (which are often referentially ambiguous) can be automatically resolved with respect to an extensional coordinate model (toponym resolution), using hybrid heuristic statistical methods. The major contributions of this research project are a corpus of text manually annotated for spatial named entities with their model correlates as a training evaluation resource [4] and a novel method to spatially ground toponyms in text.
@cite_13: This paper describes the generation of a model capturing information on how placenames co-occur together. The advantages of the co-occurrence model over traditional gazetteers are discussed and the problem of placename disambiguation is presented as a case study. We begin by outlining the problem of ambiguous placenames. We demonstrate how analysis of Wikipedia can be used in the generation of a co-occurrence model. The accuracy of our model is compared to a handcrafted ground truth; then we evaluate alternative methods of applying this model to the disambiguation of placenames in free text (using the GeoCLEF evaluation forum). We conclude by showing how the inclusion of placenames in both the text and geographic parts of a query provides the maximum mean average precision and outline the benefits of a co-occurrence model as a data source for the wider field of geographic information retrieval (GIR).
@cite_25: The availability of map interfaces and location-aware devices makes a growing amount of unstructured, geo-referenced information available on the Web. This type of information can be valuable not only for browsing, finding and making sense of individual items, but also in aggregate form to help understand data trends and features. In particular, over twenty million geo-referenced photos are now available on Flickr, a photo-sharing website - the first major collection of its kind. These photos are often associated with user-entered unstructured text labels (i.e., tags). We show how we analyze the tags associated with the geo-referenced Flickr images to generate aggregate knowledge in the form of "representative tags" for arbitrary areas in the world. We use these tags to create a visualization tool, World Explorer, tha tcan help expose the content of the data, using a map interface to display the derived tags and the original photo items. We perform a qualitative evaluation of World Explorer that outlines the visualization's benefits in browsing this type of content. We provide insights regarding the aggregate versus individual-item requirements in browsing digital geo-referenced material.
By far the most common technique to geotag digital media @cite_22 is through a process of geo-parsing text, identifying toponyms in text or metadata, and toponym resolution, mapping from a name of a place to an unambiguous geographic location @cite_15 . With respect to digital media, geotagging using text has been applied to general webpages @cite_27 @cite_14 , Wikipedia pages @cite_13 and visual media such as photos posted on Flickr with geo-referenced tags @cite_25 .