I remember the moment when I found out that Twitter had released millions of tweets that were tied to a Russian disinformation campaign, which may have effected the outcome of the 2016 US Presidential election. I was in a bar, in a backroom full of disinformation experts, and the datasets have shaped my research ever since. The multiple bombshells of Twitter data which followed were the most fruitful datasets a social data scientist with an interest in disinformation could ask for.

Fast forward a year, and Twitter has released 10 GB of tweets from 11 countries (and counting) which they have attributed to “state-backed information operations” on their platform. Researchers have analyzed the datasets individually, and I myself conducted a temporal network analysis on the first Russian datasets with Dr. Charles Kriel for the NATO Defense Strategic Communications Journal. Each in-depth analysis has served an important role in understanding how each country has individually curated processes for manipulating public opinion and information. But, as I dove further in, I couldn’t help but wonder how these individually evolving networks of information manipulation would look side-by-side and what this could tell me.

The Question

In an effort to see the forest from the trees, in this article I will present high-level visualizations of six state-backed disinformation operations on Twitter. I will periodically dive into parts I find interesting, but will leave the granular analysis for other projects and researchers. Hopefully my visualizations will spark further ideas about the strategies a country, or suite of countries, may have deployed. Maybe you will see something in my visualizations that confirms or contradicts your own knowledge. If so, I’d love to explore it with you further.

Work like this takes time and resources, and I would like to thank the Mozilla Foundation for generously granting me a Mozilla Open Source Support Award to conduct this project.

I’ve boiled down a year of curiosity and exploratory analysis into the following question: what are the similarities and differences between the evolving structures of six state-backed information operations on Twitter? It’s a strange question that can’t be answered with just any data science method, or even suite of methods.

The (method to the) answer: temporal network analysis. The process of mapping out relationships within a dataset is called network visualization, and has been used by social scientists and criminologists to understand human interactions from friend networks to crime networks. Patterns in human interaction are inherently dynamic, as they change as time passes. Factoring in that element of time unfurls a static network into a temporal one. I have chosen to conduct a temporal network analysis on six countries because I believe that it is the best way to synthesize the rich, nuanced, and complicated patterns in the data. For background on temporal network visualization itself and how it was used in this project, I’ve created this short video explainer: