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Updated: Aug 3, 2025
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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Social network analysis of Twitter interactions: a directed multilayer network approach
Austin P Logan1, Phillip M LaCasse2, Brian J Lunday2
1Directorate of Plans, Programs, and Requirements, Air Combat Command, 129 Andrews Street, Langley Air Force Base, VA 23665 USA.
Social Network Analysis and Mining
|April 12, 2023
Summary
Understanding social media audiences is key for influence. This study models Twitter interactions using social network analysis and topic modeling to identify influential users and group dynamics for better audience insights.
Area of Science:
- Social Network Analysis
- Computational Social Science
- Data Science
Background:
- Effective social media influence requires understanding target audiences.
- Social media platforms offer rich self-reported data on user sentiments and priorities.
- Analyzing online user interactions is crucial for social influence outcomes.
Purpose of the Study:
- To model user interactions on Twitter as a network.
- To identify influential users and group dynamics within online discussions.
- To demonstrate a replicable process for analyzing social media data.
Main Methods:
- Social Network Analysis (SNA) to model Twitter user interactions.
- Latent Dirichlet Allocation (LDA) for topic modeling of tweets.
- Construction of a directed multilayer network connecting users, conversations, and topics.
Main Results:
- Topically-focused social networks provide more robust identification of influential users.
- PageRank algorithm demonstrated superior performance in ranking individual influence.
- Community detection algorithms (Greedy Modular, Leiden) yielded mixed but valuable insights.
Conclusions:
- A four-step process for analyzing social media data is presented.
- The methodology is readily replicable and automatable with low effort.
- Understanding user-topic connections enhances insights into social influence and online dynamics.
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