Automating Hate: Exploring Toxic Reddit Norms with Google Perspective
Bibliographic record
Abstract
The Canadian Online Harms Legislation (COHL) proposal identifies proactive Automated Moderation as a solution to classifying and removing online content which violates norms such as hate. Emerging automated moderation algorithms include Google Perspective, a machine learning model which scores hateful features in text content as “toxicity.” This study identifies that hateful community content norms are currently emerging on volunteer user moderation platforms such as Reddit. To operationalize these concepts, a Theoretical Framework is constructed using Gorwa’s (2019) Platform Governance models and Massanari’s (2017) overview of Toxic Technoculture communities. While previous research exploring community toxicity is discussed, there is a gap in research which analyzes the Post, Comment, and Image Meme contributions of Reddit Moderator users to hateful community content norms. As such, an analysis of the Reddit community R/Metacanada is constructed which compares the toxicity of Moderator and user contributions using Google Perspective. The results of the applied Mann-Whitney U test analysis indicate that r/Metacanada Moderators and users contribute content at similar toxicity levels. Supplementing these tests, RQ1 then structures a qualitative analysis of false negative results which may emerge in the automated classification of multi-modal image content. Identifying that hate in online memes is structured through layered Signifier and Signified elements, a critical discussion is established which interprets potential marginalizing effects of the COHL’s automated moderation applying Noble’s (2018) theory of Technological Redlining. As such, this thesis immerses itself within the contemporary context of online content regulation, drawing upon existing conceptualizations and methodological approaches, offering a critical discussion of regulating hate content using automated algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".