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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.
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.
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.
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