This research will improve our understanding of how misinformation becomes woven into narratives online, how technology influences this process, and how design might be used to alter it. Online misinformation can influence public health attitudes, potentially costing billions of dollars and numerous lives. Online narratives are a critical object of inquiry because narratives are fundamental to how people construct socially shared belief systems, and they can be the primary means by which misinformation is spread online. It is therefore imperative that we develop a better understanding of the interplay between attitudes, misinformation, and narratives in the online social contexts. This research will contribute to our understanding of the complex interactions among technologically mediated social systems and public health attitudes, leading directly to new insights about how to design sociotechnical strategies for correcting misinformation that is embedded within them. More generally, this proposal will begin to explore how different design features interact with the production of narratives in the context of misinformation. Most importantly, this research will generate a set of insights about how the design of online networks can influence the correction of misinformation of many kinds.

This project will span a series of crowd-based experiments to investigate how people in online networks work together to combine misinformation to create and defend public health narratives. These experiments leverage a novel research platform for examining how people in online networks combine information to create coherent stories. The studies will consider three research questions: (1) How does information diffuse in the context of other, connected pieces of information? (2) How do different signals about information credibility influence the creation of stories? (3) How do network diversity and the content of corrective messages influence attempts to correct misinformation embedded in socially shared stories? All of these studies consider how existing public health attitudes influence the way that people in online networks process public health information. This work will extend current models of information contagion to account for the fact that individual pieces of information to which individuals are exposed depend on one another as well as the background knowledge, beliefs, and attitudes of receivers. The project will also consider how designed social signals, such as the number of 'likes' a post receives, and pre-existing attitudes interact.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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William Bainbridge
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Syracuse University
United States
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