TopicENA: Enabling Epistemic Network Analysis at Scale through Automated Topic-Based Coding

TL;DR

TopicENA combines automated topic modeling with epistemic network analysis to enable scalable, interpretable analysis of large text corpora by replacing manual coding with machine-generated topics.

Contribution

This paper introduces TopicENA, integrating BERTopic with ENA to automate concept coding and improve scalability for large-scale text analysis.

Findings

Coarse topics are better for large datasets.

Topic inclusion thresholds should be adjusted based on quality.

TopicENA successfully scales to larger datasets.

Abstract

Epistemic Network Analysis (ENA) is a method for investigating the relational structure of concepts in text by representing co-occurring concepts as networks. Traditional ENA, however, relies heavily on manual expert coding, which limits its scalability and real-world applicability to large text corpora. Topic modeling provides an automated approach to extracting concept-level representations from text and can serve as an alternative to manual coding. To tackle this limitation, the present study merges BERTopic with ENA and introduces TopicENA, a topic-based epistemic network analysis framework. TopicENA substitutes manual concept coding with automatically generated topics while maintaining ENA's capacity for modeling structural associations among concepts. To explain the impact of modeling choices on TopicENA outcomes, three analysis cases are presented. The first case assesses the effect of topic granularity, indicating that coarse-grained topics are preferable for large datasets, whereas fine-grained topics are more effective for smaller datasets. The second case examines topic inclusion thresholds and finds that threshold values should be adjusted according to topic quality indicators to balance network consistency and interpretability. The third case tests TopicENA's scalability by applying it to a substantially larger dataset than those used in previous ENA studies. Collectively, these cases illustrate that TopicENA facilitates practical and interpretable ENA analysis at scale and offers concrete guidance for configuring topic-based ENA pipelines in large-scale text analysis.