ABSTRACT
Online misinformation has been shown to be a significant threat, with measurable real world impact. This has become especially evident during the COVID-19 crisis, where online spaces saw the propagation of false or inaccurate information on healthcare, protective equipment, vaccines, and more;causing major public health repercussions. Although many video-based online platforms are used as vehicles of such misinformation and are counted as some of the most popular and influential social media platforms, current research faces a lack of systemic methodology to analyze such content and detect potential misinformation efforts. This work proposes a solution by introducing an adaptable framework. This framework provides indicators of potential misinformation campaigns, addresses issues of large data volumes, and takes into consideration multiple classes of features such as media content, engagement, and user networks. The goal of this research is to integrate into larger, user-facing systems and help members of the information community, data scientists, journalists, or policy makers to make sense of impossibly large information environments and take action based on reliable data. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
Online misinformation has been shown to be a significant threat, with measurable real world impact. This has become especially evident during the COVID-19 crisis, where online spaces saw the propagation of false or inaccurate information on healthcare, protective equipment, vaccines, and more;causing major public health repercussions. Although many video-based online platforms are used as vehicles of such misinformation and are counted as some of the most popular and influential social media platforms, current research faces a lack of systemic methodology to analyze such content and detect potential misinformation efforts. This work proposes a solution by introducing an adaptable framework. This framework provides indicators of potential misinformation campaigns, addresses issues of large data volumes, and takes into consideration multiple classes of features such as media content, engagement, and user networks. The goal of this research is to integrate into larger, user-facing systems and help members of the information community, data scientists, journalists, or policy makers to make sense of impossibly large information environments and take action based on reliable data. (PsycInfo Database Record (c) 2022 APA, all rights reserved)