Review
Misinformation vs. Situational Awareness: The Art of
Deception and the Need for Cross-Domain Detection
Constantinos-Giovanni Xarhoulacos, Argiro Anagnostopoulou, George Stergiopoulos and Dimitris Gritzalis *
Citation: Xarhoulacos, C.-G.;
Anagnostopoulou, A.; Stergiopoulos,
G.; Gritzalis, D. Misinformation vs.
Situational Awareness: The Art of
Deception and the Need for
Cross-Domain Detection. Sensors
2021, 21, 5496. https://doi.org/
10.3390/s21165496
Academic Editors:
Nikolaos Pitropakis and
Giovanni Pau
Received: 2 July 2021
Accepted: 11 August 2021
Published: 15 August 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Department of Informatics, Athens University of Economics & Business, 10434 Athens, Greece;
cxarhou@aueb.gr (C.-G.X.); anagnostopouloua@aueb.gr (A.A.); geostergiop@aueb.gr (G.S.)
* Correspondence: dgrit@aueb.gr
Abstract:
The world has been afflicted by the rise of misinformation. The sheer volume of news
produced daily necessitates the development of automated methods for separating fact from fiction.
To tackle this issue, the computer science community has produced a plethora of approaches, docu-
mented in a number of surveys. However, these surveys primarily rely on one-dimensional solutions,
i.e., deception detection approaches that focus on a specific aspect of misinformation, such as a
particular topic, language, or source. Misinformation is considered a major obstacle for situational
awareness, including cyber, both from a company and a societal point of view. This paper explores the
evolving field of misinformation detection and analytics on information published in news articles,
with an emphasis on methodologies that handle multiple dimensions of the fake news detection
conundrum. We analyze and compare existing research on cross-dimensional methodologies. Our
evaluation process is based on a set of criteria, including a predefined set of performance metrics, data
pre-processing features, and domains of implementation. Furthermore, we assess the adaptability
of each methodology in detecting misinformation in real-world news and thoroughly analyze our
findings. Specifically, survey insights demonstrate that when a detection approach focuses on several
dimensions (e.g., languages and topics, languages and sources, etc.), its performance improves, and
it becomes more flexible in detecting false information across different contexts. Finally, we propose
a set of research directions that could aid in furthering the development of more advanced and
accurate models in this field.
Keywords:
situational awareness; cyber situational awareness; misinformation; cybersecurity; fake
news; Information and Communication Technology (ICT) security; incident response; deception
detection; Cross-Domain detection
1. Introduction
Social networks and online news reading have undoubtedly become a staple of our
daily routine. However, the ease of access to any type of information provides fertile
ground for the systematic spread of falsehoods through “informative” websites or even
trusted news outlets. The term “news” includes information in the form of articles, claims,
statements, speeches, or posts that can be generated by any individual (journalist or
otherwise). The term “fake news” refers to intentionally false news that has been published
by a news source [1].
Fake news is a current problem that affects the world negatively. It is a multi-faceted
phenomenon that can be split into different categories based on its contents and the
purpose it serves. Fake news has appeared under several definitions, depending on the
individual’s point of view. Gelfert defines fake news as “purportedly factual claims that
are epistemically deficient (in a way that needs to be specified)” [
2
]. Tandoc E. et al. refer
to fake news as “news articles that are intentionally and verifiably false and could mislead
readers” [
3
]. Either definition could be considered correct depending on perspective;
however, the consensus regarding the matter is quite clear. Information described by this
Sensors 2021, 21, 5496. https://doi.org/10.3390/s21165496 https://www.mdpi.com/journal/sensors
