OffensiveLang: A Community Based Implicit Offensive Language Dataset

Fuente: arXiv
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Main Authors: Das, Amit, Rahgouy, Mostafa, Feng, Dongji, Zhang, Zheng, Bhattacharya, Tathagata, Raychawdhary, Nilanjana, Jamshidi, Fatemeh, Jain, Vinija, Chadha, Aman, Sandage, Mary, Pope, Lauramarie, Dozier, Gerry, Seals, Cheryl
Format: Preprint
Published: 2024
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author Das, Amit
Rahgouy, Mostafa
Feng, Dongji
Zhang, Zheng
Bhattacharya, Tathagata
Raychawdhary, Nilanjana
Jamshidi, Fatemeh
Jain, Vinija
Chadha, Aman
Sandage, Mary
Pope, Lauramarie
Dozier, Gerry
Seals, Cheryl
author_facet Das, Amit
Rahgouy, Mostafa
Feng, Dongji
Zhang, Zheng
Bhattacharya, Tathagata
Raychawdhary, Nilanjana
Jamshidi, Fatemeh
Jain, Vinija
Chadha, Aman
Sandage, Mary
Pope, Lauramarie
Dozier, Gerry
Seals, Cheryl
contents The widespread presence of hateful languages on social media has resulted in adverse effects on societal well-being. As a result, addressing this issue with high priority has become very important. Hate speech or offensive languages exist in both explicit and implicit forms, with the latter being more challenging to detect. Current research in this domain encounters several challenges. Firstly, the existing datasets primarily rely on the collection of texts containing explicit offensive keywords, making it challenging to capture implicitly offensive contents that are devoid of these keywords. Secondly, common methodologies tend to focus solely on textual analysis, neglecting the valuable insights that community information can provide. In this research paper, we introduce a novel dataset OffensiveLang, a community based implicit offensive language dataset generated by ChatGPT 3.5 containing data for 38 different target groups. Despite limitations in generating offensive texts using ChatGPT due to ethical constraints, we present a prompt-based approach that effectively generates implicit offensive languages. To ensure data quality, we evaluate the dataset with human. Additionally, we employ a prompt-based zero-shot method with ChatGPT and compare the detection results between human annotation and ChatGPT annotation. We utilize existing state-of-the-art models to see how effective they are in detecting such languages. The dataset is available here: https://github.com/AmitDasRup123/OffensiveLang
format Preprint
id arxiv_https___arxiv_org_abs_2403_02472
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle OffensiveLang: A Community Based Implicit Offensive Language Dataset
Das, Amit
Rahgouy, Mostafa
Feng, Dongji
Zhang, Zheng
Bhattacharya, Tathagata
Raychawdhary, Nilanjana
Jamshidi, Fatemeh
Jain, Vinija
Chadha, Aman
Sandage, Mary
Pope, Lauramarie
Dozier, Gerry
Seals, Cheryl
Computation and Language
The widespread presence of hateful languages on social media has resulted in adverse effects on societal well-being. As a result, addressing this issue with high priority has become very important. Hate speech or offensive languages exist in both explicit and implicit forms, with the latter being more challenging to detect. Current research in this domain encounters several challenges. Firstly, the existing datasets primarily rely on the collection of texts containing explicit offensive keywords, making it challenging to capture implicitly offensive contents that are devoid of these keywords. Secondly, common methodologies tend to focus solely on textual analysis, neglecting the valuable insights that community information can provide. In this research paper, we introduce a novel dataset OffensiveLang, a community based implicit offensive language dataset generated by ChatGPT 3.5 containing data for 38 different target groups. Despite limitations in generating offensive texts using ChatGPT due to ethical constraints, we present a prompt-based approach that effectively generates implicit offensive languages. To ensure data quality, we evaluate the dataset with human. Additionally, we employ a prompt-based zero-shot method with ChatGPT and compare the detection results between human annotation and ChatGPT annotation. We utilize existing state-of-the-art models to see how effective they are in detecting such languages. The dataset is available here: https://github.com/AmitDasRup123/OffensiveLang
title OffensiveLang: A Community Based Implicit Offensive Language Dataset
topic Computation and Language
url https://arxiv.org/abs/2403.02472