  The prevalence of new technologies and social media has amplified the effects
of misinformation on our societies. Thus, it is necessary to create
computational tools to mitigate their effects effectively. This study aims to
provide a critical overview of computational approaches concerned with
combating misinformation. To this aim, I offer an overview of scholarly
definitions of misinformation. I adopt a framework for studying misinformation
that suggests paying attention to the source, content, and consumers as the
three main elements involved in the process of misinformation and I provide an
overview of literature from disciplines of psychology, media studies, and
cognitive sciences that deal with each of these elements. Using the framework,
I overview the existing computational methods that deal with 1) misinformation
detection and fact-checking using Content 2) Identifying untrustworthy Sources
and social bots, and 3) Consumer-facing tools and methods aiming to make humans
resilient to misinformation. I find that the vast majority of works in computer
science and information technology is concerned with the crucial tasks of
detection and verification of content and sources of misinformation. Moreover,
I find that computational research focusing on Consumers of Misinformation in
Human-Computer Interaction (HCI) and related fields are very sparse and often
do not deal with the subtleties of this process. The majority of existing
interfaces and systems are less concerned with the usability of the tools
rather than the robustness and accuracy of the detection methods. Using this
survey, I call for an interdisciplinary approach towards human-misinformation
interaction that focuses on building methods and tools that robustly deal with
such complex psychological/social phenomena.
