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Related Experiment Video

Updated: Jul 25, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Entity graphs for exploring online discourse.

Nicholas Botzer¹, Tim Weninger¹

¹Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46656 USA.

Knowledge and Information Systems

|June 26, 2023

Summary

This summary is machine-generated.

Analyzing online discussions reveals that while conversations initially diverge, they tend to converge on popular topics. This study introduces an entity graph to map human communication patterns in social networks.

Keywords:

Entity linking Graphs Influence Online discourse Social media

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Area of Science:

Computational Social Science
Natural Language Processing
Cognitive Psychology

Background:

Online communication generates vast digital data suitable for computational analysis.
Traditional social network analysis models users as nodes and concepts as flowing between them.

Purpose of the Study:

To propose an alternative perspective for analyzing online discourse by organizing it into a static concept space called an entity graph.
To investigate the dynamics of online conversations and their predictability using this novel framework.

Main Methods:

Extraction and organization of large-scale online discourse from Reddit into an entity graph.
Quantitative experiments to assess the predictability of conversations.
Development of an interactive tool to visualize conversation trails over the entity graph.
Application of the spreading activation function from cognitive psychology.

Main Results:

Online discourse proved difficult to predict, particularly as conversations progressed.
Conversations initially diverged broadly across topics but tended to converge towards simpler, popular concepts.
The entity graph and associated tools provided compelling visual narratives of communication dynamics.

Conclusions:

The entity graph framework offers a new way to understand online communication by mapping static concepts and dynamic human interaction.
Despite unpredictability, online conversations exhibit a general trend of topic divergence followed by convergence.
Computational analysis of digital communication can yield insights into cognitive processes and social dynamics.