Practitioners must make several key decisions in the counter-disinformation program design phase. Those decisions include identifying a specific set of problems the program will address, developing a logic through which the program will address that problem, selecting between alternative activities, and deciding who will be the primary targets or beneficiaries of those activities.
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Tool spotlight: Hewlett Foundation Literature Revie
“The Hewlett Foundation commissioned this report to provide an overview of the current state of the literature on the relationship between social media; political polarization; and political “disinformation,” a term used to encompass a wide range of types of information about politics found online, including “fake news,” rumors, deliberately factually incorrect information, inadvertently factually incorrect information, politically slanted information, and “hyperpartisan” news.
The review of the literature is provided in six separate sections, each of which can be read individually but that cumulatively are intended to provide an overview of what is known—and unknown—about the relationship between social media, political polarization, and disinformation.The report concludes by identifying key research gaps in our understanding of these phenomena and the data that are needed to address them.”
Context Analysis and Problem Statements
Effective DRG programs to counter disinformation require the identification of a specific problem or set of problems in the information environment in a particular context.
DRG practitioners rely on several research methods to identify priority issues, context-specific drivers of information disorders, perpetrators and targets of disinformation, and incentives to perpetuate or mitigate disinformation. Landscape and stakeholder analyses are approaches to answer key descriptive research questions about the information environment, including identifying important modes of communication, key media outlets, perpetrators and target audiences for disinformation, and key political issues or personalities that might be the subjects of disinformation. Of note, women and members of other marginalized groups have been victims of political and sexualized disinformation, online hate, and harassment. As such, DRG practitioners should also account for uniquely targeted disinformation aimed at marginalized populations globally by conducting qualitative, quantitative, and gender sensitive, inclusive research in order to understand these important dynamics.
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Sample general research questions:
- What are the main drivers of disinformation in this context?
- What are the incentives for key actors to perpetuate or mitigate disinformation in this context?
- Through which medium is disinformation likely to have the greatest impact in this context?
- What evidence suggests our proposed activity(ies) will mitigate the problem?
- What groups are the primary targets or consumers of disinformation in this context?
- What key issues or social cleavages are most likely to be subjects of disinformation in this context?
These methods may also be explanatory, inasmuch as they identify key causes or drivers of specific information disorders.
As an exploratory option, key data collection methods often include key informant interviews (KII) with respondents identified through convenience or snowball sampling. Surveys and public opinion polls can also be valuable tools for understanding the media and information landscape. Survey questionnaire items on the media landscape can inform programming by identifying how most people get news on social or political events, what outlets are most popular among specific demographic or geographic groups, or which social or political issues are particularly polarizing. Respondents for surveys or polls, if possible, should be selected via a method of sampling that eliminates potential selection biases to ensure that responses are representative of a larger population of interest. Landscape and stakeholder analyses may also rely on desk research on primary and secondary sources, such as state administrative data (e.g. census data, media ownership records, etc.), journalistic sources like news or investigative reports, academic research, or program documents from previous or ongoing programs.
Applied Political Economy Analysis (PEA) is a contextual research approach that focuses on identifying the incentives and constraints that shape the decisions of key actors in an information environment. This approach goes beyond technical solutions to information disorders to analyze why and how key actors might perpetuate or mitigate disinformation, and subsequently, how these social, political, or cultural factors may affect the implementation, uptake, or impact of programmatic responses. Like other context analysis approaches, PEA relies on both existing research gathered and analyzed through desk review and data collection of experiences, beliefs, and perceptions of key actors.