Repository for "Leveraging Conflicts in Social Media Posts: Unintended Offense Dataset" paper, published in EMNLP 2024. All updates on this public dataset can be found in this repository.
Unintended Offense tweets (UO) collected through the method proposed in the paper are combined with negatives from hatespeech-twitter dataset to build this Unintended Offense Dataset. The details of the combination are listed below.
3 types of train & validation set are provided, under 3 different settings as the experiment section in the paper:
| Type | Size (train+val) | Positives | Negatives |
|---|---|---|---|
| Annotated | 2088 (1670+418) | UO(50+) | Founta(negatives) |
| Mixed | 5322 (4256+1066) | UO(50+) & UO(unannotated) | Founta(negatives) |
| Full | 7504 (6022+1502) | UO(all) | Founta(negatives) |
(50+ means only the tweets with offensiveness annotation >50 are included)
1 type of test set is provided under the "Mixed" setting
| Type | Size (test) | Positives | Negatives |
|---|---|---|---|
| Mixed | 524 | UO(50+) & UO(unannotated) | Founta(negatives) |
Also, The following is the biblatex of the work of Founta. Please cite their paper in any published work that uses any of resources from their work.
@inproceedings{founta2018large,
title={Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior},
author={Founta, Antigoni-Maria and Djouvas, Constantinos and Chatzakou, Despoina and Leontiadis, Ilias and Blackburn, Jeremy and Stringhini, Gianluca and Vakali, Athena and Sirivianos, Michael and Kourtellis, Nicolas},
booktitle={11th International Conference on Web and Social Media, ICWSM 2018},
year={2018},
organization={AAAI Press}
}