NDLI: Fine-grained geolocalisation of non-geotagged tweets

Content Provider	IEEE Xplore Digital Library
Author	Palpanas, Themis Paraskevopoulos, Pavlos
Copyright Year	2015
Description	Author affiliation: University of Trento, Telecom Italia - SKIL (Paraskevopoulos, Pavlos) \|\| Paris Descartes University (Palpanas, Themis)
Abstract	The rise in the use of social networks in the recent years has resulted in an abundance of information on different aspects of everyday social activities that is available online, with the most prominent and timely source of such information being Twitter. This has resulted in a proliferation of tools and applications that can help end-users and large-scale event organizers to better plan and manage their activities. In this process of analysis of the information originating from social networks, an important aspect is that of the geographic coordinates, i.e., geolocalisation, of the relevant information, which is necessary for several applications (e.g., on trending venues, traffic jams, etc.). Unfortunately, only a very small percentage of the Twitter posts are geotagged, which significantly restricts the applicability and utility of such applications. In this work, we address this problem by proposing a framework for geolocating tweets that are not geotagged. Our solution is general, and estimates the location from which a post was generated by exploiting the similarities in the content between this post and a set of geotagged tweets, as well as their time-evolution characteristics. Contrary to previous approaches, our framework aims at providing accurate geolocation estimates at fine grain (i.e., within a city). The experimental evaluation with real data demonstrates the efficiency and effectiveness of our approach.
Starting Page	105
Ending Page	112
File Size	497272
Page Count	8
File Format	PDF
ISBN	9781450338547
DOI	10.1145/2808797.2808869
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Association for Computing Machinery, Inc. (ACM)
Subject Keyword	Training Geology Earthquakes social networks Cities and towns Twitter Time measurement geotag geolocation
Content Type	Text
Resource Type	Article

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