Studying Anti-Social Behaviour on Reddit with Communalytic
Bibliographic record
Abstract
The chapter presents a new social media research tool for studying subreddits (i.e., groups) on Reddit called Communalytic. It is an easy-to-use, web-based tool that can collect, analyze and visualize publicly available data from Reddit. In addition to collecting data, Communalytic can assess the toxicity of Reddit posts and replies using a machine learning API. The resulting anti-social scores from the toxicity analysis are then added as weights to each tie in a “who replies to whom” communication network, allowing researchers to visually identify and study toxic exchanges happening within a subreddit. The chapter consists of two parts: first, it introduces our methodology and Communalytic’s main functionalities. Second, it presents a case study of a public subreddit called r/metacanada. This subreddit, popular among the Canadian alt-right, was selected due to its polarizing nature. The case study demonstrates how Communalytic can support researchers studying toxicity in online communities. Specifically, by having access to this additional layer of information about the nature of the communication ties among group members, we were able to provide a more nuanced description of the group dynamics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".