Name
Abstract | ||
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In the last decade, social media have become widely exploited sources of information concerning every aspect of life. The reason is that they permit to create and widely share information, even in mobility by exploiting mobile apps. Information available on social media are often related to public places (restaurants, museums, etc.), and users often look for interesting public places. Currently, various social media own and publish huge and independently-built corpora of geo-located data about public places, which are not linked each other. In particular, the main players are Google and Facebook.
Users searching for a public place of interest (POI) might wish to get all available information from social media. Therefore, they need an on-line aggregation engine for public places that returns an aggregated view of a place, retrieving data concerning the same place from various sources. The on-line approach is suggested by continuous variations in data within the on-line corpora, that demands for a technique that cannot rely on off-line processing.
In this paper, we address the problem by devising a novel technique to aggregate geo-located data about public places; the application context is to associate data provided by Google Places with Facebook pages concerning public places; the Klondike software tool implements this technique. Tests were conducted on a data set containing about 300 public places in Manchester (UK).
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3297280.3297576 | SAC |
Keywords | Field | DocType |
Google and Facebook, aggregating place information, geo-located places and POI, geographic information retrieval, social media | Publication,Software tool,World Wide Web,Social media,Computer science,Place of interest,Geographic information retrieval,Application Context,Mobile apps | Conference |
ISBN | Citations | PageRank |
978-1-4503-5933-7 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maurizio Toccu | 1 | 6 | 2.70 |
| Giuseppe Psaila | 2 | 722 | 192.45 |
| Davide Altomare | 3 | 0 | 0.34 |