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Proposing a conceptual framework: social media listening for public health behavior

2023-07-30 · Shu-Feng Tsao, Helen Chen, Samantha Meyer, Zahid A. Butt

Existing communications and behavioral theories have been adopted to address health misinformation. Although various theories and models have been used to investigate the COVID-19 pandemic, there is no framework specially designed for social listening or misinformation studies using social media data and natural language processing techniques. This study aimed to propose a novel yet theory-based conceptual framework for misinformation research. We collected theories and models used in COVID-19 related studies published in peer-reviewed journals. The theories and models ranged from health behaviors, communications, to misinformation. They are analyzed and critiqued for their components, followed by proposing a conceptual framework with a demonstration. We reviewed Health Belief Model, Theory of Planned Behavior/Reasoned Action, Communication for Behavioral Impact, Transtheoretical Model, Uses and Gratifications Theory, Social Judgment Theory, Risk Information Seeking and Processing Model, Behavioral and Social Drivers, and Hype Loop. Accordingly, we proposed the Social Media Listening for Public Health Behavior Conceptual Framework by not only integrating important attributes of existing theories, but also adding new attributes. The proposed conceptual framework was demonstrated in the Freedom Convoy social media listening. The proposed conceptual framework can be used to better understand public discourse on social media, and it can be integrated with other data analyses to gather a more comprehensive picture. The framework will continue to be revised and adopted as health misinformation evolves.

📄 PDF Abstract BibTeX arXiv:2308.02037

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Misinformation

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Attention 설명 없음
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
RAdam 설명 없음
Graph Self-Attention 설명 없음
HypE Hyperboloid Embeddings (HypE) is a novel self-supervised dynamic reasoning framework, that utilizes positive first-order existential queries on a KG to learn representations of…

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