@techreport{TD:102107,
	att_abstract={{The myriad geotagged posts in the social media constitute a vibrant information source that can be used to support {em geosocial search}, that is, a search for geographic locations based on user activities in online social networks and microblogging platforms. Unlike a traditional geographic search, the results of a geosocial search are not restricted to predefined entities, and may reflect events, sentiments, and other matters that are expressed in the social media. A search for ``jogging'', for instance, will indicate popular jogging places. A search for ``4-$th$ of July Fireworks'' would point out places where people watch the spectacle and tweet about it. Yet, a geosocial search is different from an ordinary Web search because there is no natural partition of the space into documents. There is a need to find new ways to effectively rank, filter, and present results. 

In this paper, we introduce a novel two-step search process of first, quickly finding relevant areas by using an arbitrarily indexed partition of the space, and second, applying clustering to the geotagged posts in the discovered areas, to present more accurate results. We propose and compare four different ranking measures for evaluating the relevance of an area to a given query. Our experiments, over a dataset of more than 40 million geotagged posts, illustrate the effectiveness of geosocial search, e.g., for finding events, or in a search based on a sentiment, in comparison to an ordinary geographic search.  Online search is supported by a hybrid partition-aware inverted index that we developed. Using our index, results are retrieved in a fraction of a second over millions of posts, even on a single standard machine. }},
	att_authors={yk140j},
	att_categories={C_P1},
	att_copyright={{ACM}},
	att_copyright_notice={{(c) ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in SIGSPATIAL 2017 {{, 2017-11-07}}.
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={Geosocial search,  spatio-temporal,  keyword search,  social media,  geotagged posts},
	att_techdoc={true},
	att_techdoc_key={TD:102107},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:102107_DS1_2017-09-27T01:20:22.144Z.pdf},
	author={Yaron Kanza and Barak Pat},
	institution={{SIGSPATIAL 2017}},
	month={November},
	title={{Where's Waldo? Geosocial Search over Myriad Geotagged Posts}},
	year=2017,
}