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Various communication and behavioral theories have been adopted to address health infodemics. However, there is no framework specially designed for social listening studies using social media data, machine learning, and natural language processing techniques. We aimed to propose a novel yet theory-based conceptual framework for infodemic research.

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Background: In the face of the proliferation of misinformation during the COVID-19 pandemic, crowdsourced debunking has surfaced as a counter-infodemic measure to complement efforts from professionals and regular individuals. In 2021, X (formerly Twitter) initiated its community-driven fact-checking program, named Community Notes (formerly Birdwatch). This program allows users to create contextual and corrective notes for misleading posts and rate the helpfulness of others' contributions.

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Objectives: COVID-19 disproportionately affected people of Black ethnicities whilst also negatively affecting the health, wellbeing and livelihoods of people living with HIV. This may have been amplified by pre-existing socioeconomic marginalisation, poorer health, and structural racism. Despite being disproportionately affected by the pandemic, little is known about lived experiences of the COVID-19 pandemic within these communities.

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Background: Social media has become a vital tool for health care providers to quickly share information. However, its lack of content curation and expertise poses risks of misinformation and premature dissemination of unvalidated data, potentially leading to widespread harmful effects due to the rapid and large-scale spread of incorrect information.

Objective: We aim to determine whether social media had an undue association with the prescribing behavior of hydroxychloroquine, using the COVID-19 pandemic as the setting.

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The proliferation of inaccurate and misleading information about COVID-19 on social media poses a significant public health concern. This study examines the impact of the infodemic and beneficial information on COVID-19 protective behaviors in an armed-conflict country. Using the protective action decision model (PADM), data were collected from 1439 participants through a questionnaire in Yemen between August 2020 and April 2021.

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