Countering Misinformation on Social Media: A Socio-Ecological Response Model

• In Event: How to Counter Misinformation Online

Abstract

RESEARCH QUESTIONS: There is an abundance of inaccurate and misleading information online, challenging social cohesion and democracy in general. This is because deliberation – an integral part of democracy – is founded on the premise that citizens can reach a common understanding about societal challenges (McKay & Tenove, 2021). In order to achieve this, citizens need to have access to accurate information and build resistance against misinformation. Existing research is discipline-focused and fragmented, and we lack a comprehensive conceptualization of how to respond to misinformation in an integrated way. This study synthesizes the existing literature along the question: Which levels of interventions have been used to respond to misinformation? Which interventions were successful? And how can these successful interventions be incorporated in a comprehensive model?

METHOD: The idea for this research endeavor emerged in a workshop which gathered researchers from the US and Europe to discuss transatlantic challenges in misinformation responses. Based on the discussions of the workshop, the researchers engaged in a narrative literature review and synthesis to collect a wide range of studies from various disciplines (e.g., political science, communication, psychology and law) to better understand responses to misinformation from a transdisciplinary perspective. The results of the synthesis were then used to conceptualize a theoretical model to counter misinformation, which we call Socio-Ecological Response Model (SERM).

RESULTS: The SERM distinguishes between agents, intervention levels, intervention types, the target of the intervention, and intervention outcomes. The agents who set up or implement interventions include governments, civil society, social media platforms, educational institutions, individuals, social networks and communities. These agents shape intervention on different levels. For example, the policy level, where governments (often together platforms and civil society agents) develop feasible policy solutions (Moore & Tambini, 2021). Such solutions include the increase in platform transparency, the formalization of platform responsibilities, the promotion of quality control, and also educational initiatives. On the organizational level, interventions are implemented by an institution, such as a fact-checking organization, a government agency, educational institutions, academic institutions, among others. For example, fact-checking organizations may cooperate with big tech companies, and engage in content moderation. Of course, platforms also engage in self-regulation. For example, Meta has created an Oversight Board, responsible for deciding which content to take down or leave on the platform (Wong & Floridi, forthcoming). Finally, on the social level, individual citizens or social groups may share accurate information, engage in public deliberation, report or flag misinformation, or correct misinformation shared by other users (e.g., Walter & Murphy, 2018). Interventions at each level can either target the platform environment, or the agency of the users. For example, policies can target the platform via platform regulation, or users via educational policies. Via changes in platform environments and empowered users, two key outcomes are achieved: a decrease in the amount of misinformation, and an increase in the amount of accurate information.

IMPACT: The SERM approaches misinformation relying on an integrated set of interventions. These interventions are driven by diverse agents, target either users or platforms, and contribute to a safer information environment. The transdisciplinary nature of the model highlights the need to approach misinformation theory holistically, using all forces at hand. The model informs future research, outlining new avenues for future research. Indeed, we see fruitful potential research agendas in the intersections of agents, levels, and targets of the model. Future research may also investigate how to increase the spread of accurate information, which is just as important as the mitigation of actual misinformation. Finally, our model can inform practitioners, such as government or public health organizations, in supporting safe information environments.

REFERENCES:
Kay, S., & Tenove, C. (2021). Disinformation as a threat to deliberative democracy. Political Research Quarterly, 74(3), 703-717.
Moore, M., & Tambini, D. (2021). Introduction. In M. Moore & D. Tambini (Eds.), Regulating Big tech: Policy responses to digital dominance (pp. 1-14). Oxford University Press.
Wong, D., & Floridi, L. (forthcoming). Meta’s Oversight Board: a review and critical assessment. Minds and Machines. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4255817
Walter, N., & Murphy, S. T. (2018). How to unring the bell: A meta-analytic approach to correction of misinformation. Communication Monographs, 85(3), 423-441.

Authors

• Leticia Bode, Georgetown University

• Raffael Heiss

• Samantha Bradshaw, American University

• Ethan Porter, George Washington University

• Elena Engel, MCI The Entrepreneurial School

• Sascha Gell

• Mark MacCarthy, Georgetown University