REVIEW 4 cited by
Hostility Detection Dataset in Hindi
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
In this paper, we present a novel hostility detection dataset in Hindi language. We collect and manually annotate ~8200 online posts. The annotated dataset covers four hostility dimensions: fake news, hate speech, offensive, and defamation posts, along with a non-hostile label. The hostile posts are also considered for multi-label tags due to a significant overlap among the hostile classes. We release this dataset as part of the CONSTRAINT-2021 shared task on hostile post detection.
Forward citations
Cited by 4 Pith papers
-
Web(er) of Hate: A Survey on How Hate Speech Is Typed
Hate speech datasets vary because curators hold different ideal types of hate, so the field should document those assumptions instead of chasing a single definition.
-
Multimodal Zero-Shot Framework for Deepfake Hate Speech Detection in Low-Resource Languages
A contrastive audio-text framework detects hate speech in synthesized speech across six languages and outperforms baselines, with a new 127k-sample dataset.
-
HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation
A tri-partite survey finds that hate speech definitions and moderation practices in country laws, platform policies, and NLP datasets are largely misaligned, and calls for a unified proactive moderation framework.
-
Explainable AI: XAI-Guided Context-Aware Data Augmentation
XAI-guided augmentation that replaces the least important words, identified by Integrated Gradients, with back-translated synonyms or paraphrases improves hate speech and sentiment classification accuracy by up to 8 p...
Discussion (0). Continue with ORCID to comment.