@techreport{TD:101326,
	att_abstract={{The advent of  Web 2.0 gave birth to a new kind of applications with content generated through the collaborative contribution of many different users. This form of content generation is believed to generate data of higher quality since the "wisdom of the crowds" makes its way into the data. However, as it is generally the case in real life, there are many issues that for different reasons, enjoy no generally accepted opinion towards them. These issues are characterised as controversial. Knowing these issues when reading the user generated content is of major importance in understanding the quality of the data and the trust that should be given to them. In this work we describe a technique that finds these controversial issues by analyzing the edits that have been performed on the data over time.  We apply our technique on Wikipedia, the world's largest known collaboratively generated database and we report our findings.}},
	att_authors={ds8961},
	att_categories={C_BB.1},
	att_copyright={{IEEE}},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2014, Volume $vol}}{{, 2015-04-13}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:101326},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101326_DS1_2014-11-14T21:04:09.955Z.pdf},
	author={Divesh Srivastava and Yannis Velegrakis and Siarhei Bykau and Flip Korn},
	institution={{IEEE International Conference on Data Engineering}},
	month={April},
	title={{Fine-Grained Controversy Detection in Wikipedia}},
	year=2015,
}