[AISWorld] CFP: ACM JDIQ Special issue on Combating Digital Misinformation and Disinformation

[Andrea Marrella]

#### 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 **************

Regular Initial submission:       April 1st, 2018
*Non-CS Initial submission:       May 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

#### Context ***********************************

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

#### Topics *************************************

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 regular 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).

##### *Non-CS submissions ************************

Given the intrinsically multidisciplinary nature of this special issue, in
addition to technical/scientific contributions
from the Computer Science field, we welcome a limited number of
challenge/vision papers from the areas of political, social
or historical sciences, able to broaden the special issue perspective and
to put the technical contributions in a live
societal context.

Such submissions have a 8 page limit and their area of provenance (e.g.,
political sciences) should be clearly indicated on the front page.

##### Format *************************************

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: http://jdiq.acm.org/authors.cfm#subm

All submissions will receive at least three reviews.

##### Contacts ***********************************

For any question, do not hesitate to contact:

- Andrea Marrella
  Information Director of  ACM Journal of Data Quality

- Tiziana Catarci
  Editor in Chief of  ACM Journal of Data Quality