If more journalists had been proficient in digital network analysis, coverage of the internet over the last ten — and especially the last four — years might have looked very different. The digitization of information continues to change society in massive ways, but investigating how information moves online is still only a miniscule niche within journalism.

Despite (or perhaps due to) a lack of understanding, online phenomena perceived as “popular” or “viral” are given immense media coverage, with less scrutiny. We know that trusted news outlets are being targeted as an inroad into the public sphere, as a way to influence and manipulate; that we don’t do more to defend against it remains a cosmic paradox in journalism.

One solution is doing more network analysis. Today, that’s too often left to scientists or researchers. Occasionally, their findings are covered by the media. But that process often takes time and happens long after the phenomenon being investigated.

In an online information environment where disinformation runs rampant, journalists need the ability to understand how information flows online and how to locate its sources. As more journalists realize the significance of understanding network analysis, it will make its way into the general journalistic toolbox.

Journalists able to do network analysis will give their audiences a vastly improved understanding of how information travels online and who’s really hidden behind user accounts and messaging networks. Social media companies have never been much help when it comes to helping their users understand how content flows on their platforms. That makes it simultaneously more difficult and more important for journalists to cover it.

While it’s a stretch to call network analysis easy, it’s clearly possible to identify coordinated and hidden activity on various social networks. You could obtain useful data via web scraping, APIs, or custom tools like NodeXL and DMI-TCAT.

By using one of these methods, you’ll be able to trace patterns and activity not visible through ordinary use of the platforms. This could be data about words or topics, user accounts and their behavior — like who they follow, who their followers follow, and what messages they broadcast and amplify.

More journalists will be inspired to learn about network analysis, presenting their findings to readers through software like Gephi. They’ll also realize that understanding the network is as crucial as understanding the story they’re reporting on. Just the ability to identify the first time a post or account appeared online can be extremely relevant for any journalist relying on digital sources.

In 2021, things like the amazing network visualizations from Erin Gallagher, comprehensive guides from Benjamin Strick, and collections of great practical examples from Paul Bradshaw will contribute to much better journalism about the internet and its role in shaping our world.