Temporal Social Network Analysis using Harvard Caselaw Access Project

Abstract

Using Natural Language Processing (NLP) and graph algorithms on 100 years of state and federal legal decisions, the names of lawyers and their relationships are extracted using transformer-based language models and then analyzed with Social Network Analysis (SNA) to build social networks and then study how these social networks change over time. Data was grouped by jurisdiction and decade, and various metrics were calculated for each group. Changes in these metrics over time are substantial and show that SNA must take into account the passage of time when looking at data sets spanning large intervals of time. More generally, because individual metrics can be sensitive to scale and other factors, it was found that looking at the changes in metrics over time was often more informative and consistent across different jurisdictions. This may have implications for analysis in other domains as well.