ABSTRACT
Amidst the recent COVID pandemic, the extensive spread of misinformation has led to such significant societal ramifications that the World Health Organization has termed this issue as an 'infodemic'. Addressing this COVID infodemic problem requires careful analysis and correct interpretation of the COVID related information claims. While this research need has led to the creation of multiple datasets for aiding COVID misinformation detection, these current datasets primarily include textual data to serve this purpose. Prior works involving these datasets has made limited use of the implicit graphic image contents in this regard. Existing literature indicates a lack of proper utilization of the valuable insights that can be derived from the visual cues found in online COVID articles, sites, and social media posts. In order to address this research gap, we perform a preliminary analysis of three different multimodal COVID datasets, which are traditionally used for COVID information claim processing. We specifically collect the images plus infographic elements from the online websites listed in these datasets, and come up with five notable categories for these COVID visual cues while studying them. To our knowledge, this initial survey study is a first of its kind that investigates multimodal COVID datasets, and leads to a unique collection of COVID image artifacts. This paper produces a strategic taxonomy of these various COVID visual cues as an important contribution, and makes an effort to advocate for the need of explicitly multimodal datasets that account for both textual and image data to successfully analyze COVID information claims. © 2023 IEEE.
ABSTRACT
When studying cybersecurity, the emphasis is generally given the personal information protection and the safeguarding of the technology on which the information is stored. Cybersecurity attacks, which can occur in multiple forms, can seriously affect the involved stakeholders mentally, and this impact aspect tends to be underestimated. With the human mind being a significant attack target, psybersecurity has begun gaining prominence as an important field of study. In this survey paper, we explore psybersecurity as an emerging interdisciplinary area within the human security domain of cybersecurity and conduct a detailed study of its causes plus effects. We discuss existing research work, which is relevant to this field of psybersecurity, and present a nifty organization of the surveyed literature, which is classified into three notable categories. With psychiatric engineering gaining prominence as a new impactful attack vector, a psybersecurity attack (PSA) primarily targets the human mind. We study the relations between cybersecurity and cyberpsychology, as well as between psychiatric engineering (PE) and social engineering (SE) from an interdisciplinary perspective. We perform a unique analysis of both PE and SE as PSA, linking them to Cialdini's six principles and their associated elements, as causes for PSA. We then show how to connect these causal components of PSA to the eight cyberpsychology dimensions through a tabular map that we have developed. We also discuss the emergence of COVID-driven PSA with a focus on the psybersecurity of online healthcare information (OHI) users, including potential ways of protecting the users of OHI from the increase of psybersecurity threats. We conclude this survey study by looking at the potential scope of future work in psybersecurity, including new research directions and open problems plus research questions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.