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JDIQ-CDMD 2019 : Special issue of the ACM Journal of Data and Information Quality (ACM JDIQ) on Combating Digital Misinformation and Disinformation


When N/A
Where N/A
Submission Deadline Apr 1, 2018
Notification Due Jul 1, 2018
Final Version Due Jan 10, 2019
Categories    NLP   artificial intelligence

Call For Papers

Special issue of the ACM Journal of Data and Information Quality (ACM JDIQ) on
Combating Digital Misinformation and Disinformation

*Guest editors *
Naeemul Hassan, University of Mississippi
Chengkai Li, University of Texas at Arlington
Jun Yang, Duke University
Cong Yu, Google Research

Important dates and timeline:
Initial submission: April 1st, 2018
First review: July 1st, 2018
Revised manuscripts: Sept. 1st, 2018
Second review: Nov. 1st, 2018
Camera-ready manuscripts: Jan. 10th, 2019
Publication: April 1st, 2019

Spread of misinformation and disinformation is one of the most serious challenges facing the news industry, and a threat to democratic societies worldwide. The problem has reached an unprecedented level via social media, where contents can be created and disseminated to a large audience with little to zero cost and revenues are driven by click-through rates. Researchers from multiple disciplines have proposed various strategies, built automated and semi-automated systems, and recommended policy changes across the media ecosystem. Recently, researchers have also explored how artificial intelligence techniques, particularly machine learning and natural language processing, can be leveraged to combat falsehoods online.

In this special issue of JDIQ, we aspire to provide an overview of innovative research primarily at the intersection of information credibility, machine learning, and data science, from theory to practice, with a focus on combating misinformation and disinformation.

Specific topics within the scope of the call include, but are not limited to, the following:

- Automated question-answering for fact-checking
- Crowdsourced fact-checking
- Data collection, labeling and extraction for fact-checking
- Detection of fake-news spreading social bots
- Knowledge bases for fact-checking
- Models and methods for tracking the propagation and derivation of online data
- Multi-modal deception detection
- Natural language processing approaches to fact checking
- Role of AI agents in fake news propagation
- Role of metadata and provenance management in assessing veracity of online information
- Semantic parsing and verification of fake news
- Sustainable fact-checking framework
- Techniques to detect and limit misinformation and disinformation in social media
- Truth discovery from structured and unstructured data

*Expected contributions:*
We welcome two types of contributions:
- Research manuscripts reporting mature results (up to 25 pages)
- Experience papers that report on lessons learned from addressing specific issues within the scope of the call. These papers should be of interest to the broad data quality community. (12+ pages plus an optional appendix)

JDIQ welcomes manuscripts that extend prior published work, provided they contain at least 30% new material, and that the significant new contributions are clearly identified in the introduction.

Submission guidelines with Latex (preferred) or Word templates are available here:
All submissions will receive at least three reviews.

Prof. Tiziana Catarci
Dipartimento di Ingegneria Informatica, Automatica e Gestionale "A.Ruberti"
Universita' degli Studi di Roma "La Sapienza"
Via Ariosto 25, 00185 Roma

Phone +39-0677274007
Fax +39-0677274007

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