[AISWorld] CFP - Information Processing & Management Journal - Special Issue on Dis/Misinformation Mining from Social Media

[Fattane Zarrinkalam]

*CALL FOR PAPERS*

Information Processing & Management Journal
Impact Factor: 4.787
https://www.journals.elsevier.com/information-processing-and-management

Special Issue on Dis/Misinformation Mining from Social Media
http://ls3.rnet.ryerson.ca/?page_id=1077

*AIM AND SCOPE*

In the last 10 years, the dissemination and use of social media have grown
significantly worldwide. Online social media have billions of users and are
able to record hundreds of data from each of its users. The wide adoption
of social media has resulted in an ocean of data which presents an
interesting opportunity for performing data mining and knowledge discovery
in a real-world context. The enormity and high variance of the information
that propagates through large user communities influences the public
discourse in society and sets trends and agendas in topics that range from
marketing, education, business and medicine to politics, technology and the
entertainment industry. This influence can however act as a double-edged
sword, since it can also introduce threats to the community, if it is
rooted in dissemination of disinformation, i.e. purposefully manipulated
news and information, or misinformation, i.e. false and incorrect
information, on social media. In recent years, the potential threats of
dis/misinformation have been the subject of huge controversy in different
domains like public healthcare systems, socioeconomics, business and
politics. For instance, the circulation of scientifically invalid
information and news can negatively affect the way the public responds to
the outbreak of a pandemic disease, like COVID-19. Threats can also be
posed to the legitimacy of an election system by enabling opponent
campaigns to shape the public opinion based on conspiracy theories stemmed
from false information. Mining the contents of social media to recognize
the instances of misinformation and disinformation is a very first step
towards immunizing the public society against the negative impacts they
could introduce.

Traditional research on dis/misinformation mining from social media mainly
focuses on descriptive methods such as fake news detection and propagation
analysis, malicious bot detection, fact-checking social media content, and
detecting the source of claims and rumors. The main distinguishing focus of
this special issue will be the use of social media data for building
diagnostic, predictive and prescriptive analysis models that can be used to
understand how and why dis/misinformation is created and spread, to uncover
hidden and unexpected aspects of dis/misinformation content, and to
recommend insightful countermeasures to restrict the circulation of
dis/misinformation and alleviate their negative effects. The ultimate goal
is to immunize the social media against dis/misinformation and improve the
trustworthiness of the social content and the socio-economic and business
systems working based on the insights mined from social media. The main
focus of the special issue is on proposing models and methods for tackling
dis/misinformation in real-world scenarios.

In this special issue, we solicit manuscripts from researchers and
practitioners, both from academia and industry, from different disciplines
such as computer science, big data mining, machine learning, social network
analysis and other related areas to share their ideas and research
achievements in order to deliver technology and solutions for mining
dis/misinformation from social media.


*TOPICS OF INTEREST*

We solicit original, unpublished and innovative research work on all
aspects around, but not limited to, the following themes:

* Descriptive models on fake new and malicious bot detection.
* Explainable AI for detection of dis/misinformation.
* User behavior analysis and susceptibility prediction with regard to
dis/misinformation in social media.
* Trust and reputation in social media.
* Dis/misinformation propagation modeling and trace analysis.
* Prescriptive countermeasure methods against formation and circulation of
misinformation
* Predicting misinformation and bias in news on social media.
* Predictive models for early detection of hoax spread in social media.
* Social influence analysis on online social media including discovering
influential users and social influence maximization.
* Assessing the influence of fake news on advertising and viral marketing
in social media.
* New datasets and evaluation methodologies to help predicting
dis/misinformation in social media
* User modeling and social media including predicting daily activities,
recurring events Determining user similarities, trustworthiness and
reliability.
* Social media and information/knowledge dissemination such as topic and
trend prediction, prediction of information diffusion patterns, and
identification of causality and correlation between
events/topics/communities.
* Merging internal (proprietary) data with social data.


*IMPORTANT DATES FOR THE SPECIAL ISSUE*

* Submission deadline: January 20, 2021
* First Notification: April 1, 2021
* Revisions Due: May 1, 2021


*GUEST EDITORS (Alphabetical)*

* Ebrahim Bagheri, Ryerson University, Toronto, Canada, bagheri at ryerson.ca
* Huan Liu, Arizona State University, Arizona, United States,
huanliu at asu.edu
* Kai Shu, Illinois Institute of Technology, Chicago, Illinois, kshu at iit.edu
* Fattane Zarrinkalam, Ryerson University, Toronto, Canada,
fzarrinkalam at ryerson.ca

*SUBMISSION INFORMATION*

Papers submitted to this special issue for possible publication must be
original and must not be under consideration for publication in any other
journal or conference. Previously published or accepted conference papers
must contain at least 30% new material to be considered for the special
issue.

All papers are to be submitted through the journal editorial submission
system. At the beginning of the submission process in the submission
system, authors need to select "Mis/Dis Information Mining from Social
Media" as the article type. All manuscripts must be prepared according to
the journal publication guidelines which can also be found on the website
provided above. Papers will be evaluated following the journal's standard
review process.