-------- Forwarded Message -------- Subject: [AISWorld] CFP - Springer Social Network Analysis and Mining (SNAM) - Special Issue on Tackling COVID-19 Infodemic Date: Tue, 21 Jul 2020 21:05:11 -0700 From: Kai Shu kshu@iit.edu To: aisworld@lists.aisnet.org CC: Miriam Metzger metzger@ucsb.edu, Huan Liu huanliu@asu.edu

*Call For Papers* ================== Social Network Analysis and Mining - Special Issue on Tackling COVID-19 Infodemic Website: https://www.springer.com/journal/13278/updates/18175124

*Description*================== While the COVID-19 pandemic continues its global devastation, numerous accompanying challenges emerge. One important challenge is to efficiently and effectively use recently gathered data and find computational tools to combat the COVID-19 infodemic. An infodemic is an overabundance of information – some accurate and some not – occurring during an epidemic. Rampant conspiracy theories, disinformation, misinformation, and various types of scams can spread fast by taking advantage of human gullibility, fear, and ignorance. Therefore, there is a pressing need to manage the infodemic to help people find trustworthy information and reliable guidance during this global public health crisis. AI for Good holds a lot of potential for solving societal problems including combating the infodemic, while challenges remain. It is important to integrate theories from different disciplines to help with the COVID-19 crisis.

In this special issue, we provide an interdisciplinary forum for researchers and practitioners to combat the COVID-19 infodemic. We expect novel research to study the understanding, detection, mitigation, and measurement of the COVID-19 infodemic and potential future outbreaks. To facilitate further research in COVID-19 infodemic, this special issue welcomes interdisciplinary research articles, new open-access datasets, repositories, and benchmarks, broadening research on crisis informatics and its development.

*Topics of interest include, but are not limited to:* ==================================== - Disinformation/misinformation detection and mitigation - Fact-checking and credibility assessment - Computational tools to understand, measure and contain infodemic - Diffusion and intervention of infodemic - Bias, transparency, and fairness - Economical impacts of infodemic - Infodemic monitoring across countries, regions, and cultures - Individual and societal impacts of COVID-19 infodemic - Epidemiology, public health, and infodemic - Psychology, marketing, and behavioral insights - Data science, applied maths, and physics for infodemic - Society, ethics, trust, and governance for infodemic - Attribution and source tracing of COVID-19 infodemic - Spatio-temporal infodemic analysis - Social network analysis - AI for COVID-19 - Sociological and psychological analysis of infodemic - COVID-19 infodemic datasets, benchmarks, and repositories

*Submission Instructions* ================== Articles reporting original and unpublished research results pertaining to the above topics are solicited. We welcome two types of research contributions: (1) Research manuscripts reporting novel methodologies and results (up to 20 pages); (2) Benchmark, Datasets, Repositories, and Demonstration Systems (up to 8 pages).

Submitted articles will follow an academic review process. Manuscripts must be prepared according to the instruction for authors available at the journal webpage and submitted through the publisher's online submission system, available at https://www.editorialmanager.com/snam/default.aspx. Please note: when submitting, please choose the correct special issue, i.e. "*SI: Tackling COVID-19 Infodemic*".

*Important Dates* ================== Submission deadline: *October 15, 2020* Notification: November 25, 2020 Camera-ready deadline: December 30, 2020

*Guest Editors * ================== Kai Shu, Illinois Institute of Technology, kshu@iit.edu Miriam Metzger, University of California, Santa Barbara, metzger@ucsb.edu Huan Liu, Arizona State University, huanliu@asu.edu

All questions can be directed to Kai Shu at kshu@iit.edu. _______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org