Common mistakes students/journalists make with fact checking and source verification

The first misconception I would think of, when students or journalists work with Truly Media, is that the system is going to magically solve very difficult disinformation items. Even though this is a unique fact checking platform with plenty of tools and awarded workflows, it is still teamwork that’s going to solve the complexity of the issues that are inserted into it. 

From that point of view and in terms of ‘mistakes’, the challenge usually is explainability, which I feel is very important for journalists or users of the platform, for them to be able to understand the exact reason behind the use of each fact checking tool, because not every tool applies to every purpose. 

So, using the wrong tool for what they initially thought would be relevant is quite common. 

That’s where our clarifications and on-hands training emerges as critical. We don’t just provide a platform, but we ensure that the users of the platform are well equipped to understand the differences between tools and the relevancy of different approaches, so that they become more efficient and faster in their analyses. 

Also, even if a tool has been chosen in the right context, it’s still sometimes a challenge for a user to get to know exactly how to use it, but that’s an issue that gets resolved more easily. Additional training and testing usually normalizes it.  

Another key ‘mistake’ is users not sticking to the process. It actually doesn’t automatically qualify as a  mistake, as it can be rather a design flaw that needs adjustment, but what is widely observed -especially with not so large newsrooms- is that journalists can easily go off the known track of ‘collecting’, ‘organizing’ and ‘verifying’; a process we know is effective. 

The platform has a collaborative nature for a reason: it allows professionals with different backgrounds or students with different traits and skills to work together to reach a conclusion. However, on numerous occasions some users tend to work in silos within the platform, isolating themselves from the team, thus reducing the usefulness of collaborative fact checking. 

Lastly, I would point to a few times during which users relied heavily on the result output, even though models (especially those helping with video and audio deep fakes) follow a probabilistic pattern and not a deterministic one. 

Thus, it’s always considered safe to draw an initial conclusion, which has to then be further supported by additional research, which needs to be done in a less technical and more traditional research framework. 

So, labeling a story as fake just because an algorithm gave a good probability and proceeding with the actual publication should always be avoided. And Truly Media empowers users to use tools, but also follow the right steps for complete news verification (with a dedicated checklist for every fact check), even if this means to work with sources, upload files that support or refute a statement etc.