Given a tweet, the systems have to decide whether it presents misogynistic contents or non-misogynistic ones. It is a binary classification task.
Publication
Elisabetta Fersini, Paolo Rosso, Maria Anzovino (2018) Overview of the Task on Automatic Misogyny Identification at IberEval 2018. Proceedings of the Third Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval 2018).
Competition
Language
Spanish
English
NLP topic
Abstract task
Dataset
Year
2018
Publication link
Ranking metric
Accuracy
Task results
| System | Precision | Recall | F1 | CEM | Accuracy Sort ascending | MacroPrecision | MacroRecall | MacroF1 | RMSE | MicroPrecision | MicroRecall | MicroF1 | MAE | MAP | UAS | LAS | MLAS | BLEX | Pearson correlation | Spearman correlation | MeasureC | BERTScore | EMR | Exact Match | F0.5 | Hierarchical F | ICM | MeasureC | Propensity F | Reliability | Sensitivity | Sentiment Graph F1 | WAC | b2 | erde30 | sent | weighted f1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 14-exlab.c.run3 | 0.8146 | ||||||||||||||||||||||||||||||||||||
| JoseSebastian.c.run1 | 0.8146 | ||||||||||||||||||||||||||||||||||||
| SB.c.run4 | 0.8134 | ||||||||||||||||||||||||||||||||||||
| 14-exlab.c.run1 | 0.8122 | ||||||||||||||||||||||||||||||||||||
| 14-exlab.c.run2 | 0.8122 |
