Extract from George Socha’s article “Visualize: Analyzing Connections Between Communications”
In almost every investigation and lawsuit, we need to analyze communications. We look at the content of the communications, of course, but we should also look at the communications’ context. In particular, the connections between the communications. We analyze this information to figure out who was communicating with whom, when, where, how, about what, and why. And, often, we can do that most effectively with strong visualization tools.
Today’s post is about visualizing connections, but first let’s turn to content.
The Standard Approach to Understanding Communications
In analyzing communications, we tend to focus on the content of the communications. We examine their text first then, increasingly, on their metadata as well. To accomplish this, we turn to a bevy of capabilities. Tools and techniques we might use include active learning, supervised machine learning, and technology assisted review (TAR), AI models, anomaly detection, high precision active learning, image recognition, pattern recognition, sentiment analysis, stylometry, and translation.
Sometimes we are on an exploratory mission which can be open-ended. We might, for example, be trying to identify affirmative defenses to assert, unearth additional witnesses to contact, or develop a different explanation for what caused the alleged harm. That mission might also have a pre-set list of objectives. Perhaps the complaint sets forth three causes of action, each cause of action has several elements that the plaintiff needs to prove, and we are looking for evidence to support or refute each of those elements.