Small Reddit Corpus as a Network

This project explored how a corpus from a discussion forum might be explored as a network. I chose a small Reddit corpus for this purpose. The small Reddit corpus presents a sampling of 100 highly active subreddits from 2018 gathered from Pushshift.io and has open source access through ConvoKit (at a minimum).

As a social network, Reddit presents a discussion forum-based structure with membership to many communities. It is a structure other social networks use, so the purpose here was to explore how turning a post into multiple conversations opens up research questions into community and well-being.

The structure of this network follows:

• Each top-level comment in a post is a conversation thread without a link back to a topic node. This conversation definition makes the network unipartite with user comments spanning threads and therefore subreddits.
• Latent meaning: engaged user participation; the conceptualization of information propagation or diffusion is somewhat different here than one might expect of a social network. Users replying in multiple subreddits still act as an information bridge that could affect community aspects like conversational tone and norms. The extent to which this is true of bridges is under research using this network structure.
• Nodes: users
• Edges: reply-to

Structure

• The python notebook presents some code and exploration while convo_structure_parsing.py exists to reproduce the network file.
• The network data is provided as both a multi-graph and a digraph to allow for different research questions to be explored. You will find separate data files and network cards labeled as such (the filename below woth * for the network type)
  • reddit_small_corpus_network_*.gml.gz is a compressed .gml network file.
  • The network card network_card_*.json is a concise summary of the network.

License and disclaimers

The license applicable to the network data is ODC-BY (see DATA_LICENSE.txt).

The content of posts has not been filtered for profanity or offensive references.

Resources

ConvoKit
Reddit Corpus (small)

Notes:

• This is a work in progress.
• The current implementation is specific to Reddit datasets loaded using ConvoKit.
• Some shorthand variable names refer to object types: "convo" for Conversation, "utt" for Utterance.
• The main graph type is a MultiDiGraph, useful for representing multiple replies between the same users (parallel edges). The corpus can also be represented in a DiGraph where the number of replies between 2 unique users is instead an edge weight (opt for the digraph method). This approach loses most utterance information but may be useful for some research questions if variables can be captured as proxies for edge and node attributes.

I am making the following assumptions:

• Each top level comment within a post is represented as a conversation, with the top level comment removed.
• Nodes are users and edges are replies (out-going is a reply from one user (source) to another user (target)).
• AutoModerator and Auto- may make posts but do not appear to be a concern for having initiated top-level comments.
• deleted users and comments are represented as [deleted] in the raw data; comments containing this substitution remain but deleted users are recoded to one unique ID "deleted_#" per branch/top-level comment in the conversation. This was deliberate to 1) preserve the conversation reply-to chain, and 2) to make the assumption that deleted users within a branch could be some of the same person having a back-and-forth discussion. This is a simplifying assumption. For instance, the more time distributed between earlier messages in the dataset's distribution of timestamps and 2018 (when this data was gathered), the more confounding variables and variance we cannot account for in terms of why we see [deleted] and which users those tags represent. This unknown area may affect the simplifying assumption of using one deleted_speaker_ID per branch in a post versus changing them all to be unique per top-level comment branch.