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, documented 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.
Misinformation vs. Situational Awareness: The Art of Deception and the Need for Cross-Domain Detection