How harassment and hate speech policies have changed over time: Comparing Facebook, Twitter and Reddit (2005–2020)
Bibliographic record
Abstract
Abstract Social media platforms make choices about what content is and is not permissible on their platforms. For example, choices about if and how to deal with online harassment and hate speech are growing problems in many online settings. But these choices are often opaque, can vary from platform to platform, and can change over time with little notice. This study examines the ways Facebook, Twitter, and Reddit have defined harassment and hate speech, as well as who they frame as responsible for dealing with harassment and hate speech over time. Using content analysis, the policy structures that house relevant policies, the policy documents themselves, and blog posts are examined. The results illustrate a phased approach to defining harassment and hate, which has become increasingly complex and nuanced over time. Additionally, this work shows a compounding view of who is responsible, which began with users but over time has come to include the platform itself, technology, and external actors such as civil society groups. This paper highlights continued opacity and increasing complexity while also providing contextual historical information necessary for both future research and platform governance decisions.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".