Περίληψη:
Micro-blogging services such as Twitter have gained enormous popularity over the last few years leading to massive volumes of user generated content. A portion of this content is shared via geo-aware mobile devices, such as smartphones. Pieces of information shared on such a device can be tagged with the user's location, conditional on the user's settings. These geostamps enable a number of mainstream applications, such as emergency response, disease tracking, news reporting, and advertising. Unfortunately, informative geostamps are typically sparse, since content is often shared via devices that do not support geo-tagging, such as desktop or laptop computers. In addition, even if a mobile device is used, a flawed geo-location service can lead to missing geostamps, or geostamps that are too general to be informative. In this work, we address this sparsity issue via a new approach that identifies users attached to a given location of interest, such as a city. We then focus on retrieving specific tweets at a finer granularity within the given location, such as specific blocks within a city. Our approach leverages the correlation between strong connectivity in the social graph and proximity in the real world, while utilizing both textual tweet content and Twitter's underlying social graph. Previous relevant work assumes that all required Twitter data is available without access restrictions. This is an unrealistic assumption, since Twitter limits the number of data requests per user and charges a subscription fee for unrestricted access. Therefore, in order to increase the number of practitioners and applications that can benefit from our work, we optimize our method to work with the minimum amount of queries to the Twitter API. Finally, our experiments demonstrate the efficacy of our work via both a quantitative and qualitative evaluation. © 2015 Elsevier Ltd. All rights reserved.
Συγγραφείς:
Kotzias, D.
Lappas, T.
Gunopulos, D.
Λέξεις-κλειδιά:
Laptop computers; Location; Mobile devices, Access restriction; Data sparsity; Emergency response; Information shared; Micro-blogging services; Qualitative evaluations; Strong connectivity; User-generated content, Social networking (online)
