Unless you were homeschooled, you’ve likely experienced the social dynamics of a high school classroom. There are popular kids, cheaters, class clowns, quiet bookworms, and everyone in between. But if you ever needed help with your calculus homework, who did you turn to? We’re guessing, more than likely, it wasn’t a popularity contest. You probably knew exactly which of your classmates had a real grasp of calculus and the willingness to help.
This innate understanding humans have of social hierarchies in the classroom setting is the basis for what we call Classroom Theory—the idea that within any knowledge community, be it a classroom or an entire industry, it is essential to be able to identify authentic expertise, and that popularity is often not the key indicator. Our objective is to pinpoint genuine experts using technology rather than depending on human instinct, aware that these individuals will not always be the most popular, prominent, or polished.
Classroom Theory is inspired by a branch of mathematics called social network analysis—looking at patterns of connections between people to map out and understand underlying social structures, interactions, and dynamics. Researchers have used it to study friend groups, communication patterns, and more.
These same principles can be applied in domains like investment management and financial services. The true expert voices for Fintech on LinkedIn (for example) are not always those with the most followers, loudest presence, or slickest marketing. By analyzing the web of interconnections and interactions, we can begin tracing ideas back to their original sources and identify people and organizations that may not be the most popular based on platform metrics like number of followers and interactions, but they have attracted the respect of their peers and have influence over the various other more popular members shaping and promoting the current narratives within their industry networks.
By analyzing the web of interconnections and interactions, we can begin tracing ideas back to their original sources...
For lack of a better way to put it, natural language processing is a natural fit for Classroom Theory, combined with network analysis to supercharge this approach. Its ability to detect nuance in language and extract it as usable data enables the identification and quantification of true thought leaders versus those simply posturing, regurgitating, and amplifying other people’s ideas. In other words, it identifies the calc whizzes from among the popular kids, class clowns, cheaters, and bookworms.
In the echo chamber of financial media and content marketing, Classroom Theory provides a framework to cut through the chatter. Smarter evaluation of connections and content enables the identification of genuinely insightful voices, even if they don't top the typical leaderboards. It formalizes our intuitive human social wiring into quantitative analytics.
Smarter evaluation of connections and content enables the identification of genuinely insightful voices, even if they don't top the typical leaderboards.
Of course, modeling social fabrics is technically complex. But breakthroughs in network science and machine learning offer powerful tools to put Classroom Theory into practice. While still evolving, these techniques will soon help analysts mine for nuggets of wisdom within massive and noisy networks, allowing them to tune out the collective chatter and zero in on the voices of true influence.
Ayano is a virtual writer we are developing specifically to focus on publishing educational and introductory content covering AI, LLMs, financial analysis, and other related topics. Humans are currently responsible for ideation, prompt engineering, fact-checking, copy editing, and overall guidance and training—including finalizing translations, while LLMs cover initial research, analysis, copywriting, and drafting translations into multiple languages.
