The election has revealed how news narratives are artificial; consume with that in mind
I wanted to expand on this point from Dave Wasserman:
What he noted here is pretty consistent with what I teach in my media intro course here at Lehigh: stories, to some extent, are artificial constructs. Sometimes they are more solid than others because they have the benefit of time and thought, but in breaking or developing news situations they should be consumed with some care.
News narratives are built by turning lists of facts (data, quotes, information) into stories, and then aggregates of stories become larger narratives. This is the process of reporting. Journalism isn’t a list of facts. It is an attempt to make meaning out of facts.
In a slow news environment, journalists have time to get around a story as much as possible, try to build in as many of perspectives as they can. But as news is unfolding, it is more difficult. Data and perspective are by definition incomplete, but the public is hungry to know more.
The complication is that when journalism is built by data, it is built by default by a factor out of a journalist’s control: how the data comes to us. Additionally, it is built by how we choose to sort data. Some examples:
Election Night: Why do we even have it? Why is it an all-night TV fest rather than just waiting for complete results?
The order data comes in: States report on their own schedule, at their own pace. Within states, some counties report at their own pace, and within counties it’s made up of precincts that vary wildly. What comes in first sets the tone for how you see later data.
The random order of state calls: It’s built on data. When a news org feels confident enough, they’ll call a state. That all 50 states aren’t called at once creates artificial feelings of momentum.
You can play with this idea by reimagining how narrative would look if you reported results by region, or alphabetically. How you choose to order the data determines how your story starts, and how it unfolds. But election night is based on data flow that is out of your control. Your task is to create narrative based on incomplete data and lots of unknowns.
I went to bed on Election Night when the narrative was “Trump could win this.” But on its face that is either a true or false statement. He will either win or he won’t. And by the next day, it became clear that wasn’t true. But it was as true as could be known at the time based on incomplete data. You see the rub? It was true in the sense that it was the best version of the truth known at the time based on data we have, but the complete data set made it objectively false; we just didn’t have access yet to the latter.
The results are the results. They have a truth on their own. THE truth. The final tally is the story, and yet we tell a story before that because news cycles and public wants require such things. The idealistic way would be to say we just be patient and wait for the final story. But the public doesn’t want it, and truthfully the data are public enough that non-journalists will make it available.
Journalists make sense of things in real time, but it’s an artificial construct.
So, does this make the narratives bad? No. It means the public swings of attention and emotion could be greatly helped by some media literacy here.
Think about what happened on Election Night. Trump went up big. It felt like momentum; he was getting a lot of state calls and he was leading in a lot of states. But the momentum was artificial; there were always the same number of votes in the tally, but the order we got them created impressions, both at the district level and the state level. We knew from previous reporting that the mail vote in many, many states was going to skew heavily Democratic. The comeback was inevitable. But unknown was how big the comeback would be, and even the knowledge of what was to come was battling the feeling that Trump was up big.
I do not think he means this, unless he really is just that uncurious. This is a ploy to shape narrative, to shape how the public sees the counting of all votes after Election Night. Journalists have a specific role here; they need to report the data but also to help the public understand the events that shape the data, because if not it creates a vacuum that allows someone like Trump to sow doubt about a pretty normal process.
The reason I teach my intro course the way I do — and I *love* teaching that course, y’all — is to help students (often freshmen/sophomores) grapple with how to approach news better and how to read narratives better. News is never going to get it perfect and some of this depends on how we as consumers deal with news we encounter.
What people often declare media bias is actually just incomplete narratives because either the data isn’t final or journalists can’t verify something. That makes narratives ripe for hijacking by bad actors. It’s what’s happening now. The news has been broadly telling us why Biden was able to erase a 600,000-vote lead here in Pennsylvania, but there is so much uncertainty in what data will come that it creates an opening.
We need better media literacy. And data literacy. And better reporting built to address those deficiencies.