Fwd: JDIQ Call for Papers: Special issue on Combating Digital Misinformation and Disinformation

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Fwd: JDIQ Call for Papers: Special issue on Combating Digital Misinformation and Disinformation

Jonathan Morgan
Interesting and timely CFP... -J

---------- Forwarded message ----------
From: Tiziana Catarci, ACM JDIQ Editor-in-Chief <[hidden email]>
Date: Fri, Nov 17, 2017 at 8:00 AM
Subject: JDIQ Call for Papers: Special issue on Combating Digital
Misinformation and Disinformation
To: [hidden email]

ACM Journal of Data
and Information Quality

*Special issue 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


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

*Important dates and timeline:*
Initial submission: April 1, 2018
First review: July 1, 2018
Revised manuscripts: September 1, 2018
Second review: November 1, 2018
Camera-ready manuscripts: January 10, 2019
Publication: April 1, 2019

For further information and author instructions please visit jdiq.acm.org
or contact Paolo Missier <[hidden email]> or Naeemul Hassan
<[hidden email]>.

<https://optout.acm.org/unsubscribe.cfm?re=jmorgan@...&rl=CFP> to
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Copyright 2017, ACM, Inc.

Jonathan T. Morgan
Senior Design Researcher
Wikimedia Foundation
User:Jmorgan (WMF) <https://meta.wikimedia.org/wiki/User:Jmorgan_(WMF)>
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