COMPSCI5107 Web Science MSc - 2024–25
This coursework analyses user interaction patterns and discussion dynamics within the r/InvestmentClub subreddit using network analysis techniques. Spanning a decade (2012–2022) of activity, it combines data engineering, graph theory, statistical metrics, and topic modeling to provide a structural overview of an online financial discussion community.
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Data Processing and Object Modeling:
- Cleaned and parsed Reddit JSON files (submissions + comments).
- Created schema maps and dataclasses to model objects as Python DTOs.
- Reconstructed hierarchical post-comment trees using BFS.
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Graph Visualisation:
- Author Interaction Graph: Directed, weighted graph showing comment relationships between users.
- Post Discussion Graph: Tree-like graph showing nested comment structure for individual submissions.
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Network Metrics and Analysis:
- Superuser Influence: Evaluated network cohesion using connected components and rich club coefficient.
- Z-Score Roles: Classified users as questioners or answerers based on posting behavior.
- Virality Score: Combined cascade size and comment lifespan to score post popularity.
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Additional Research:
- Temporal Trends: Submission and upvote activity across days, weeks, and years.
- User Engagement: Submission timelines of top users, identifying consistent vs. burst participation.
- Topic Clustering: KMeans clustering of submission titles using TF-IDF and PCA for 2D visualisation.
| Folder/File | Description |
|---|---|
Web-Science-Coursework.pdf |
Original coursework specification. |
Reddit-Network-Analysis-Report.pdf |
Technical report summarising the entire coursework. |
Reddit-Network-Analysis-Notebook.ipynb |
Full data pipeline and analysis notebook. |
charts/ |
Interactive charts (z-score, virality, superuser metrics). |
graphs/ |
HTML graph visualisations of author and post interactions. |
schemas/ |
Scripts to infer and generate JSON schema definitions. |
dtos/ |
Data Transfer Objects used to map Reddit JSON to Python classes. |
objects/ |
Pickled post/comment structures for reuse. |
Superuser Influence Graph
- Reveals the centrality of user
Zurevu, whose removal significantly fragments the network. - Secondary rich clubs emerge when the top user is removed, indicating hierarchical influence.
Modified Z-Score Chart
- Power-law distribution of questions and answers by users.
- Behaviour of "bot" accounts to post more answers than questions.
- Increased participation correlates with more questioning behaviour.
Topic Clustering (TF-IDF + KMeans)
- 20 distinct topic clusters identified from ~19k submission titles.
- Themes included tech stocks, macroeconomics, oil prices, and transaction strategies.
The goal of this coursework was to investigate the structure and behaviour of online communities through a combination of data processing, graph visualisation, and analytical techniques — using Python, NetworkX, Vega-Altair, and scikit-learn within a Jupyter Notebook environment.
- Process explained, irrelevant users not removed, and Data is summarised sufficiently (4/5)
- Thorough Analysis conducted and the data interpreted well (20/20)
- Three analyses conducted, data is processed and graphs are drawn. However, it is no clear what are your objectives? What research questions you were trying to answer?[12 each 36/45]
- Analysis conducted creatively – especially looking form multiple perspectives (20/20)
- 8/10 (some limitations in addressing multiple RQs in task 3)
Grade: 88/100