This project will help improve the quality of conversations and information online by identifying the constraints that regulate cross-partisan animosity and disinformation across different social media platforms. Combining the strengths of political communication and socioeconomic theories with the methodological rigor of computational and experimental approaches, this research will identify: (1) which regulators, or strategies, are suitable or most effective for combating disinformation and cross-partisan animosity online; (2) how their strengths vary across behaviors and social media platforms; and (3) how these regulators interact, at times undermining or supporting each other. Scholars once thought that online social media platforms would bring in a new era of democratic discussion and debate. However, scholars and users alike are now mostly concerned about the dark side of these platforms - problems such as incivility, cross-partisan animosity, and disinformation are all commonplace online. While there have been efforts to combat these problems - such as the use of moral suasion to curb incivility and media literacy to curb misinformation - the approaches thus far lack a unified theoretical framework that allows for a systematic exploration of the solution space.
This research will develop a framework connecting three of the modalities that regulate behavior online and offline: (1) Norms constrain through the sanctions or rules of a community. (2) Market constrains through price. (3) Architecture - built environment or code in online space - constrains through the structural burdens it imposes. The impact of these modalities on disinformation and cross-partisan animosity will be examined by developing a broad range of methodological approaches, spanning fields such as machine learning, network science, and causal inference. First, the project will contribute rich datasets and scalable machine learning and network science approaches for identifying cross-partisan animosity and disinformation online. Second, this project will bring together the theoretical strengths of legal and political communication scholarship and the computational strengths of computer and information sciences to combat problematic behaviors online. It will investigate the efficacy of different modalities of regulation through natural and randomized experiments and identify their interdependencies using structural equation modeling. Third, it will determine the generalizability of strategies by examining the behavior and efficacy of regulators across different platforms. Finally, most approaches that address problematic behaviors online treat individuals as the unit of analysis. However, structural regulators act upon communities. This project will overcome issues that generally undermine research at the individual level by performing comparative analyses across not just individuals but also communities.
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.
