I don’t know how to save the news media. I don’t know what will happen to this industry. I don’t have a prediction about what news will be 5 years from now or even 5 minutes from now. But news media organizations seem unlikely to survive if they continue to operate under assumptions that they have inherited from our previous ideas about ideas, information, and knowledge which were informed by the strengths and limitations of paper.
At its most basic, journalism assumes not only that some events are more important than others, but that all but the most important simply will not be covered. But the binary barrier imposed by the internet is so low that, when in doubt, we post it. We may contextualize it as a draft or as incomplete, but we post it. For evidence of this, just look at how arXiv is being used as a repository of draft versions of research papers locked behind the walls of closed-access academic journals.
That means that one of the most important imprimaturs has lost not only some of its luster but also much of its meaning. Newsworthiness used to really mean paper-and-ink worthiness. Without those physical limitations on what fits in print, newsworthiness is no longer solely determined by the news media, but by readers, individually and collectively.
While that means we’ve lost the assumption of a shared base of information, the arbitrariness of the criteria for newsworthiness was apparent every time you looked at the news. Are polls 2 years before an election news? Are murders news? Is a “snow emergency” news and not just a hyper-excited weather report? And are “person in the street” interviews ever, ever, ever news?
The internet and machine learning
Both our experiences on the internet and machine learning’s embrace of chaotic models may also be weakening our confidence in journalism’s commitment to interpreting events as narratives. The news media traditionally have seen narratives everywhere: There are a handful of main actors who bring about certain events that then head to resolution, even if few of the stories wrap up as nicely as they do in fiction. But now that we can learn and explore every tendril for as long as we want, we can pursue complications and complexities that often make narratives too simple a structure.
Indeed, our experiences on the internet and with machine learning are challenging the very theory of change that stories assume. That theory says that things happen because of understandable causes. A story on the latest economic downturn may headline the cause as the Federal Reserve, a corrupt financial system, trade wars, or computerized trading, and these may well be important elements of an explanation. But they are never (never?) the sole causes. The world is far too complex and delicately intertwingled for that.