I 👍 your Hate: Emojis as Infrastructural Platform Violence on Telegram
Bibliographic record
Abstract
Emojis are a ubiquitous form of online expression. In this paper, we explore emojis as affordances that enact and sustain discursive violence via toxic content. We take a case study approach by focusing on Chismes Frescos Medellin (Fresh Gossip Medellin), a Colombian Telegram group with over 125,676 members. Relying on Communalytic, we collected 98,729 publicly accessible posts. Next, we subdivided the posts into 3,155 toxic and 95,574 non-toxic posts using Detoxify, a popular machine-learning classifier. We explored and compared the two subsets through statistical analysis and thematic analysis. Our findings show that emojis—and specifically, emojis suggesting positive emotions such as 👍 and 😁—are often used to accompany toxic speech in ways that indicate the approval and normalization of toxic speech. Overall, our study points to the need to pay closer attention to how affordances can enable symbolic forms of violence on digital platforms in unexpected ways.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".