Pith. sign in

REVIEW 2 cited by

One-step and Two-step Classification for Abusive Language Detection on Twitter

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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

arxiv 1706.01206 v1 pith:J72Q2CJ6 submitted 2017-06-05 cs.CL

Ji Ho Park , Pascale Fung This is my paper · ORCID
classification cs.CL
keywords abusiveapproachclassificationlanguageone-stepdetectionf-measuretwitter
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Automatic abusive language detection is a difficult but important task for online social media. Our research explores a two-step approach of performing classification on abusive language and then classifying into specific types and compares it with one-step approach of doing one multi-class classification for detecting sexist and racist languages. With a public English Twitter corpus of 20 thousand tweets in the type of sexism and racism, our approach shows a promising performance of 0.827 F-measure by using HybridCNN in one-step and 0.824 F-measure by using logistic regression in two-steps.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph

    cs.CL 2024-12 conditional novelty 6.0 of 10

    MetaTox constructs a meta-toxic knowledge graph from toxic corpora and injects retrieved triplets into LLM prompts, improving toxicity detection and lowering false positives, especially out-of-domain.

  2. Rating for Parents: Predicting Children Suitability Rating for Movies Based on Language of the Movies

    cs.CL 2019-08 conditional novelty 5.0 of 10

    A script-only model with attention, genre, and emotion features predicts MPAA ratings with a weighted F1 of 78.03%, beating a threshold baseline, an SVM, and a CNN.

Pith tools