Paper Info
Title
AllegatorTrack: Combining and reporting results of truth discovery from multi-source data
Abstract
In the Web, a massive amount of user-generated contents is available through various channels, e.g., texts, tweets, Web tables, databases, multimedia-sharing platforms, etc. Conflicting information, rumors, erroneous and fake contents can be easily spread across multiple sources, making it hard to distinguish between what is true and what is not. How do you figure out that a lie has been told often enough that it is now considered to be true? How many lying sources are required to introduce confusion in what you knew before to be the truth? To answer these questions, we present AllegatorTrack, a system that discovers true claims among conflicting data from multiple sources.
Year
DOI
Venue
2015
10.1109/ICDE.2015.7113396
Data Engineering
Keywords
Field
DocType
application program interfaces,data mining,api,allegatortrack,multisource data,truth discovery,data models,computational modeling,computer architecture,maximum likelihood estimation,gold
Data science,Multi source data,Data mining,Data modeling,Confusion,Computer science,Lying,Maximum likelihood,Web tables,Database
Conference
ISSN
Citations 
PageRank 
1084-4627
8
0.50
References 
Authors
11
4
Name
Order
Citations
PageRank
Dalia Attia Waguih180.50
Naman Goel2113.60
Hossam M. Hammady3412.78
Laure Berti-Equille458849.90