Great question, and it’s one we’ve spent some time on. We have a post coming soon on the details of how we find and statistically model narratives present in all kinds of media, but there’s a big picture question here too: Can we get some generalized set of narratives that we can use to divide new issue spaces into meaningful chunks? The answer to this is basically no. Think of the financial wellbeing space and the stories we tell ourselves about how people get and stay rich or get and stay poor. One of the core narratives there, which we call the Robin Hood narrative, includes this story pattern:
Because of a history of unequal distribution, the world is out of balance. Now it’s greedy rich people hoarding the money and poor people living on their scraps. What we need is to take money from rich people and corporations and give it directly to poor and middle class people. That will put the world back in balance.
That’s a story pattern that appears in many different specific forms. You see discussion of “income inequality” and policy proposals like “universal basic income”. Even the Cares Act stimulus checks are part of a Robin Hood narrative (the solution to financial wellbeing is to just give money to people experiencing financial stress).
But that’s not a story pattern that generalizes to other social issues. There’s not a useful form of Robin Hood for racism, for instance, or gender. The stories we tell ourselves about how race works or how gender works or how our natural environment and the climate work have different protagonists, conflicts, and other elements.
Like you, we are interested in tools that can generalize across stories and generate new, effective stories. Narrative modeling (both the conceptual models of what the narratives are and the statistical models of which stories are in which narratives) helps us do that in one way. Poverty narrative models don’t generate new ideas about stories for climate, for instance, but they do generate ideas for story opportunities and threats about poverty. A narrative model about Equity Gap can “see” that audiences are consuming more poverty-related content that centers inequities after a specific story moment like the George Floyd murder and tell storytellers to build on that. Or a narrative model can see that audiences are consuming lots of “Vote for X” right around the election and give storytellers the clues they need to counter-narrate the story that only politicians control our financial and economic fates.
There are story tools that help us generalize across issues. Narratives are story patterns about some specific thing like poverty. Characters in narratives usually don’t generalize. But some units of narratives can stretch across issues. The ones we find most useful are values and emotional arcs. Those too are statistically modelable, and knowing that an audience loves Security-focused stories, for example, can be very helpful across issues.
