Overview of the HASOC Track at FIRE 2025: Abusive Meme Identification — Shadows Behind the Laughter
Bibliographic record
Abstract
The rapid rise of social media has unfortunately led to increased online abuse, with memes where images combined with short and provocative texts – becoming a common vehicle for hateful or derogatory content. Detecting such abusive memes is especially challenging in low-resource languages like Bangla, Hindi, Gujarati, and Bodo, where annotated datasets are limited. To address this gap, we develop a multilingual abusive meme dataset for these four Indic languages, annotated with five labels: sentiment, sarcasm, vulgarity, abuse, and target. In the associated shared task, 20 unique teams submitted over 306 system runs. Performance was evaluated using Macro F1, with the best scores reaching 0.6275 (Bangla), 0.6570 (Hindi), 0.6750 (Gujarati) and 0.6312 (Bodo). This article provides a brief overview of the task, dataset construction, system results, and key methodological approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".