Scaling up truth discovery

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The evolution of the Web from a technology platform to a social ecosystem has resulted in unprecedented data volumes being continuously generated, exchanged, and consumed. User-generated content on the Web is massive, highly dynamic, and characterized by a combination of factual data and opinion data. False information, rumors, and fake contents can be easily spread across multiple sources, making it hard to distinguish between what is true and what is not. Truth discovery (also known as fact-checking) has recently gained lot of interest from Data Science communities. This tutorial will attempt to cover recent work on truth-finding and how it can scale Big Data. We will provide a broad overview with new insights, highlighting the progress made on truth discovery from information extraction, data and knowledge fusion, as well as modeling of misinformation dynamics in social networks. We will review in details current models, algorithms, and techniques proposed by various research communities whose contributions converge towards the same goal of estimating the veracity of data in a dynamic world. Our aim is to bridge theory and practice and introduce recent work from diverse disciplines to database people to be better equipped for addressing the challenges of truth discovery in Big Data.

Original languageEnglish
Title of host publication2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1418-1419
Number of pages2
ISBN (Electronic)9781509020195
DOIs
Publication statusPublished - 22 Jun 2016
Event32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
Duration: 16 May 201620 May 2016

Other

Other32nd IEEE International Conference on Data Engineering, ICDE 2016
CountryFinland
CityHelsinki
Period16/5/1620/5/16

Fingerprint

Ecosystems
Fusion reactions
Scaling
Big data
World Wide Web
User-generated content
Tutorial
Modeling
Fusion
Ecosystem
Information extraction
Social networks
Data base
Rumor

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

Cite this

Berti-Equille, L. (2016). Scaling up truth discovery. In 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 (pp. 1418-1419). [7498359] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDE.2016.7498359

Scaling up truth discovery. / Berti-Equille, Laure.

2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1418-1419 7498359.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Berti-Equille, L 2016, Scaling up truth discovery. in 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016., 7498359, Institute of Electrical and Electronics Engineers Inc., pp. 1418-1419, 32nd IEEE International Conference on Data Engineering, ICDE 2016, Helsinki, Finland, 16/5/16. https://doi.org/10.1109/ICDE.2016.7498359
Berti-Equille L. Scaling up truth discovery. In 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1418-1419. 7498359 https://doi.org/10.1109/ICDE.2016.7498359
Berti-Equille, Laure. / Scaling up truth discovery. 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1418-1419
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