Wardley Mapping Fake News

The following is a guest post by Mike HaberSusan Almon, and Jeroen Ermers.

On March 5th, we attended an online workshop to get some hands-on practice with Wardley Mapping. The topic of the day was Disinformation, and our group focused on mapping how to recognize fake news — truth or not, technological fakes, deep fakes, videos, pictures, and all kinds of things that come to mind when we hear that phrase.

If recognizing fake news is the capability, who would the user be? As a group, we decided that it would be useful to start with the general public as the user, since we thought government agencies really ought to understand this issue.

Image courtesy of Mike Haber

After some discussion, we made our first map. Looking at our value chain, we can recognize fake news through how it contrasts against reliable information, which comes from reliable sources. What makes them reliable? Well, they are verifiable and actually verified in various ways. You get things like fact-checkers and Snopes, blue ticks (on Twitter), reputable news sources, science and peer review, Wikipedia, and then also things like algorithmic fact-checkers you can use.

Image courtesy of Mike Haber

As we worked to place each item on the evolutionary cycle, we got the impression that some items could fit in multiple places, depending on your perspective. For example, fake news and propaganda have been around for such a long time that you could say it’s a commodity. Or perhaps you can go buy forms of it, as a product. But actually crafting individual items of propaganda and making sure that they’re effective is a skill. You have to create it from scratch, and people are coming up with new ways of infecting news that haven’t been done before, which is absolutely a genesis thing. So, it really depends on your perspective.

Image courtesy of Jeroen Ermers

We thought things like reliable sources were kind of bespoke, in that they have to be invented and require work to make sure that they’re created, while online fact checkers like Snopes are more of a product.

We were surprised to find that we felt algorithmic fact-checkers actually moved across the entire evolutionary spectrum. For instance, you can write a fact checker for deep fake videos and put it online as a commodity service, but as soon as you’ve done that, people are going to try to beat it, so you have to keep on inventing new ones. We wondered if we were allowed to do that in Wardley Mapping, but it seemed like something that was happening in this case.

Here Mike Haber walks us through the group’s thought process and map:

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