demonstration

iCheck: computationally combating "lies, d--ned lies, and statistics"

Authors:

You Wu,

Cong YuAuthors Info & Claims

SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

Pages 1063 - 1066

https://doi.org/10.1145/2588555.2594522

Published: 18 June 2014 Publication History

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Abstract

Are you fed up with "lies, d---ned lies, and statistics" made up from data in our media? For claims based on structured data, we present a system to automatically assess the quality of claims (beyond their correctness) and counter misleading claims that cherry-pick data to advance their conclusions. The key insight is to model such claims as parameterized queries and consider how parameter perturbations affect their results. We demonstrate our system on claims drawn from U.S. congressional voting records, sports statistics, and publication records of database researchers.

References

[1]

Sarah Cohen, James T. Hamilton, and Fred Turner. Computational journalism. Communications of the ACM, 54(10):66--71, 2011.

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[2]

Sarah Cohen, Chengkai Li, Jun Yang, and Cong Yu. Computational journalism: A call to arms to database researchers. In CIDR 2011.

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[3]

Rob Ennals, Beth Trushkowsky, and John Mark Agosta. Highlighting disputed claims on the Web. In WWW 2010.

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Google Scholar

[4]

François Goasdoué, Konstantinos Karanasos, Yannis Katsis, Julien Leblay, Ioana Manolescu, and Stamatis Zampetakis. Fact checking and analyzing the Web. In SIGMOD 2013.

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[5]

You Wu, Pankaj K. Agarwal, Chengkai Li, Jun Yang, and Cong Yu. On "one of the few" objects. In KDD 2012.

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[6]

You Wu, Pankaj K. Agarwal, Chengkai Li, Jun Yang, and Cong Yu. Toward computational fact-checking. PVLDB, 7(7), 2014.

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Cited By

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Bindris NCristianini NLawry J(2020)Claim Consistency Checking Using Soft LogicMachine Learning and Knowledge Extraction10.3390/make20300092:3(147-171)Online publication date: 6-Jul-2020
https://doi.org/10.3390/make2030009
Samarinas CHsu WLee M(2020)Latent Retrieval for Large-Scale Fact-Checking and Question Answering with NLI training2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00147(941-948)Online publication date: Nov-2020
https://doi.org/10.1109/ICTAI50040.2020.00147
Jo STrummer IYu WWang XYu CLiu DMehta NBoncz PManegold SAilamaki ADeshpande AKraska T(2019)Verifying Text Summaries of Relational Data SetsProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3300074(299-316)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3300074
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Index Terms

iCheck: computationally combating "lies, d--ned lies, and statistics"
1. Information systems
  1. Information systems applications
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Data types and structures

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Information & Contributors

Information

Published In

SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

June 2014

1645 pages

ISBN:9781450323765

DOI:10.1145/2588555

General Chairs:
Curtis Dyreson
Utah State University, USA
,
Feifei Li
University of Utah, USA
,
Program Chair:
M. Tamer Özsu
University of Waterloo, Canada

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2014

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SIGMOD/PODS'14

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SIGMOD

SIGMOD/PODS'14: International Conference on Management of Data

June 22 - 27, 2014

Utah, Snowbird, USA

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SIGMOD '14 Paper Acceptance Rate 107 of 421 submissions, 25%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

View all

Bindris NCristianini NLawry J(2020)Claim Consistency Checking Using Soft LogicMachine Learning and Knowledge Extraction10.3390/make20300092:3(147-171)Online publication date: 6-Jul-2020
https://doi.org/10.3390/make2030009
Samarinas CHsu WLee M(2020)Latent Retrieval for Large-Scale Fact-Checking and Question Answering with NLI training2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00147(941-948)Online publication date: Nov-2020
https://doi.org/10.1109/ICTAI50040.2020.00147
Jo STrummer IYu WWang XYu CLiu DMehta NBoncz PManegold SAilamaki ADeshpande AKraska T(2019)Verifying Text Summaries of Relational Data SetsProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3300074(299-316)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3300074
Bindris NSudhahar SCristianini N(2018)Fact Checking from Natural Text with Probabilistic Soft LogicAdvances in Intelligent Data Analysis XVII10.1007/978-3-030-01768-2_5(52-61)Online publication date: 5-Oct-2018
https://doi.org/10.1007/978-3-030-01768-2_5
Wu YAgarwal PLi CYang JYu C(2017)Computational Fact Checking through Query PerturbationsACM Transactions on Database Systems10.1145/299645342:1(1-41)Online publication date: 9-Jan-2017
https://dl.acm.org/doi/10.1145/2996453

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On "one of the few" objects