I 👍 your Hate: Emojis as Infrastructural Platform Violence on Telegram
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.021 | 0.003 |
| 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 it