Non-Random Patterns in Stochastic Social Network Evolution

Study of whether macroscopic stability emerges from microscopic stochasticity in the user-user interaction network obtained from the r/Documentaries subredit.

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• Course: Complex Data Analysis 2025
• Professor: André Falcão
• Group 5:
  • Maria João Vicente (44489)
  • Pedro Fanica (54346)
  • Quentin Weiss (66292)

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Research Questions

1. Equilibrium dynamics: Are transition probabilities stationary and is detailed balance approximately satisfied over time?
2. Hub persistence: Are there any overlapping hubs that retain structural roles across snapshots, and how does burstiness affect hub collapse?
3. Predictive stability: Can motif, sentiment, and activity features predict community persistence?
4. Sentiment coevolution: Does sentiment polarization (VADER) co-evolve with community structure and indicate stability?

Dataset metadata (from the source website)

• Scope: Timestamps from their creation up to October 2018.
• Structure:
  • Each subreddit has its own corpus (subreddit-[name]).
  • Each post + comment thread is a conversation.
  • Each comment or post is an utterance.
  • Each user is a speaker (identified by Reddit username).
• Utterance-level fields: ID, speaker, conversation ID, reply-to ID, timestamp, text, score, gilding info, stickied status, permalink, and author flair.
• Conversation-level fields: Title, number of comments, domain, subreddit name, gilding, stickied status, and author flair.
• Corpus-level fields: Subreddit name, total posts, total comments, and number of unique speakers.
• Usage: Can be downloaded as a zip file or loaded with convokit, and combined with other subreddit corpora for cross-community analysis.
• Notes:
  • Some subreddit data may be incomplete or contain broken thread links.
  • Large subreddits may have very large corpus files.
  • Speaker counts may be inflated due to duplicates in preprocessing.
  • Dataset is beta and subject to updates for completeness and data consistency.

Dataset Choice

• This dataset was mentioned in the article:
  • S. Kumar, W.L. Hamilton, J. Leskovec, D. Jurafsky. Community Interaction and Conflict on the Web. World Wide Web Conference, 2018.
• This article was very reliant on sentiment analysis and the study reveals that a small group of reddit communities initiate most intercommunity conflicts, which form echo chambers, harm user activity, and can be predicted and mitigated using an LSTM-based early-warning model.
• From there we looked at datasets in Reddit corpus by subreddit to find the full r/Documentaries dataset (340 MB)