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  <description><![CDATA[UMBC ebiquity research group papers]]></description>
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    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/451/The-ICWSM-2009-Spinn3r-Dataset"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/429/Mining-Social-Media-Communities-and-Content"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/426/Geolocating-Blogs-From-Their-Textual-Content"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/413/Blog-Link-Classification"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/412/BolgVox-Learning-Sentiment-Classifiers"/>
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    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/396/Expert-Search-using-Internal-Corporate-Blogs"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/394/Enabling-Semantic-Ecoblogging-and-Bioblitzes"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/381/Approximating-the-Community-Structure-of-the-Long-Tail"/>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/451/The-ICWSM-2009-Spinn3r-Dataset">
  <title><![CDATA[The ICWSM 2009 Spinn3r Dataset]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/451/The-ICWSM-2009-Spinn3r-Dataset</link>
  <description><![CDATA[The dataset, provided by Spinn3r.com, is a set of 44 million blog posts made between August 1st and October 1st, 2008. The post includes the text as syndicated, as well as metadata such as the blog's homepage, timestamps, etc. The data is formatted in XML and is further arranged into tiers approximating to some degree search engine ranking. The total size of the dataset is 142 GB uncompressed, (27 GB compressed). 
This dataset spans a number of big news events (the Olympics; both US presiden...]]></description>
  <dc:date>2009-05-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/429/Mining-Social-Media-Communities-and-Content">
  <title><![CDATA[Mining Social Media Communities and Content]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/429/Mining-Social-Media-Communities-and-Content</link>
  <description><![CDATA[Social Media is changing the way people find information, share
knowledge and communicate with each other. The important factor
contributing to the growth of these technologies is the ability to
easily produce “user-generated content”. Blogs, Twitter, Wikipedia,
Flickr and YouTube are just a few examples of Web 2.0 tools that are
drastically changing the Internet landscape today. These platforms
allow users to produce and annotate content and more importantly,
empower them to share...]]></description>
  <dc:date>2008-12-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/426/Geolocating-Blogs-From-Their-Textual-Content">
  <title><![CDATA[Geolocating Blogs From Their Textual Content]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/426/Geolocating-Blogs-From-Their-Textual-Content</link>
  <description><![CDATA[Mashups showing the geographic location of the authors of social media content are popular. They generally depend on the authors reporting their own location. For blogs, auto-mated geolocation strategies using IP address and domain name are not adequate for determining an author’s location. Instead, we detail textual geolocation techniques suitable for tagging social media data, facilitating development of geo-graphic mashups and spatial reasoning tools.]]></description>
  <dc:date>2009-03-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/413/Blog-Link-Classification">
  <title><![CDATA[Blog Link Classification]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/413/Blog-Link-Classification</link>
  <description><![CDATA[Blog links raise three key questions: Why did the author make the
link, what exactly is he pointing at, and what does he feel about it?
In response to these questions we introduce a link model with three
fundamental descriptive dimensions where each dimension is designed
to answer one question. We believe the answers to these questions can
be utilized to improve search engine results for blogs. While proving
this is outside the scope of this paper, we do prove that knowing the
rhetoric...]]></description>
  <dc:date>2008-03-31</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/412/BolgVox-Learning-Sentiment-Classifiers">
  <title><![CDATA[BolgVox: Learning Sentiment Classifiers]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/412/BolgVox-Learning-Sentiment-Classifiers</link>
  <description><![CDATA[Performing sentiment analysis upon a topic, specified by key words, without prior knowledge about the key words is a difficult task. With the growth of the blogosphere researchers, corporations, and politicians, among others are very interested in applying sentiment detection to blogs. To accommodate the demands from myriad users, with similarly diverse desires, a sentiment analysis engine for blogs must discover domain specific features relevant to queries in order to accurately assess the s...]]></description>
  <dc:date>2007-04-10</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/406/Detecting-Commmunities-via-Simultaneous-Clustering-of-Graphs-and-Folksonomies">
  <title><![CDATA[Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/406/Detecting-Commmunities-via-Simultaneous-Clustering-of-Graphs-and-Folksonomies</link>
  <description><![CDATA[We present a simple technique for detecting communities by utilizing both the link structure and folksonomy (or tag) information that is readily available in most social media systems. A simple way to describe our approach is by defining a community as a set of nodes in a graph that link more frequently to within this set than outside it and they share similar tags. Our technique is based on the Normalized Cut (NCut) algorithm and can be easily and efficiently implemented. We validate our met...]]></description>
  <dc:date>2008-08-24</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/396/Expert-Search-using-Internal-Corporate-Blogs">
  <title><![CDATA[Expert Search using Internal Corporate Blogs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/396/Expert-Search-using-Internal-Corporate-Blogs</link>
  <description><![CDATA[Weblogs, or blogs enable a new form of communication on the Internet. In this paper, we discuss blogs within a large corporation, and show their potential as a source of evidence to the expert search task. We describe characteristics of such blogs along multiple dimensions, and identify their utility to sub-problems within expert search. We finally discuss the use of blogs when combined with additional sources of information available within corporations.]]></description>
  <dc:date>2008-07-24</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/394/Enabling-Semantic-Ecoblogging-and-Bioblitzes">
  <title><![CDATA[Enabling Semantic Ecoblogging and Bioblitzes]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/394/Enabling-Semantic-Ecoblogging-and-Bioblitzes</link>
  <description><![CDATA[People currently create Eco-blogs: stories about wildlife they
have seen or observations they've made. Similarly, citizen and
scientists work together on Bioblitzes to comprehensively report
as many species as possible from an area. Currently, none of this
information is easily discovered or integrated.  We developed and
have tested two tools that aim to make it easier for individual
scientists and citizens to convert their information to RDF and
OWL. Of 1200 Blogger BioBlitz observati...]]></description>
  <dc:date>2008-05-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/381/Approximating-the-Community-Structure-of-the-Long-Tail">
  <title><![CDATA[Approximating the Community Structure of the Long Tail]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/381/Approximating-the-Community-Structure-of-the-Long-Tail</link>
  <description><![CDATA[In many social media applications, a small fraction of the members are
highly linked while most are sparsely connected to the network. Such a
skewed distribution is sometimes referred to as the "long
tail". Popular applications like meme trackers and content aggregators
mine for information from only the popular blogs located at the head
of this curve. On the other hand, the long tail contains large volumes
of interesting information and niches. The question we address in this
work is ...]]></description>
  <dc:date>2008-03-31</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/374/Web-2-0-Mining-Analyzing-Social-Media">
  <title><![CDATA[Web 2.0 Mining: Analyzing Social Media]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/374/Web-2-0-Mining-Analyzing-Social-Media</link>
  <description><![CDATA[Social media systems such as blogs, photo and link
sharing sites, wikis and on-line forums are estimated
to produce up to one third of new Web content. One
thing that sets these ”Web 2.0” sites apart from traditional
Web pages and resources is that they are intertwined
with other forms of networked data. Their standard
hyperlinks are enriched by social networks, comments,
trackbacks, advertisements, tags, RDF data and
metadata. We describe recent work on building systems
that ana...]]></description>
  <dc:date>2007-10-10</dc:date>
 </item>
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