Digital misinformation has been flagged as a major risk of the 21st century, with an estimated cost of $78 billion to the global economy each year. Given this scope, we propose to characterize how misinformation is spread via messenger platforms (e.g. WhatsApp). Specifically, we seek to: 1. Identify metrics of potential misinformation (Aim 1). This is based on the hypothesis that although message contents are highly private, proxy markers can be used to identify potential misinformation. 2. Understand the base-rate by which misinformation is shared via messaging applications (Aim 2). This is founded on the hypothesis that misinformation is endemic on messaging platforms, and thus needs to be documented. 3. Identify "super spreaders" responsible for sending and receiving a large volume of misinformation (Aim 3). Here, we hypothesise that a small group of super spreaders are responsible for the bulk of misinformation-sharing on messaging applications. The thrust of this work aligns with both government priorities and the grant's thematic areas, providing actionable findings that are timely amidst a worldwide surge of misinformation.
Study Type
OBSERVATIONAL
Enrollment
1,000
Yale-NUS College
Singapore, Singapore
RECRUITINGWhatsApp Usage
Participants were asked a series of questions daily related to their WhatsApp usage behaviors - in particular, their message-forwarding, personal chat and group chat behaviors.
Time frame: 1 week, starting from date after baseline survey completion
Changes in fear with regards the COVID-19 situation across 1 week
1 item each day on fear specifically of the COVID-19 situation (measured on a 4 point scale: min = 1, max = 4; higher scores indicating greater fear about the outbreak).
Time frame: 1 week, starting from date after baseline survey completion
Changes in amount of thinking about the COVID-19 situation across 1 week
1 item each day on how much they thought about the outbreak that day (measured on a 5 point scale: min = 1, max = 5; higher scores indicating more thinking about the outbreak).
Time frame: 1 week, starting from date after baseline survey completion
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