Resources and benchmark corpora for hate speech detection: a systematic review
Hate Speech in social media is a complex phenomenon, whose detection has recently
gained significant traction in the Natural Language Processing community, as attested by …
gained significant traction in the Natural Language Processing community, as attested by …
A literature review of textual hate speech detection methods and datasets
F Alkomah, X Ma - Information, 2022 - mdpi.com
Online toxic discourses could result in conflicts between groups or harm to online
communities. Hate speech is complex and multifaceted harmful or offensive content …
communities. Hate speech is complex and multifaceted harmful or offensive content …
Is chatgpt better than human annotators? potential and limitations of chatgpt in explaining implicit hate speech
Recent studies have alarmed that many online hate speeches are implicit. With its subtle
nature, the explainability of the detection of such hateful speech has been a challenging …
nature, the explainability of the detection of such hateful speech has been a challenging …
Sentiment analysis in the era of large language models: A reality check
Sentiment analysis (SA) has been a long-standing research area in natural language
processing. It can offer rich insights into human sentiments and opinions and has thus seen …
processing. It can offer rich insights into human sentiments and opinions and has thus seen …
Hatexplain: A benchmark dataset for explainable hate speech detection
Hate speech is a challenging issue plaguing the online social media. While better models
for hate speech detection are continuously being developed, there is little research on the …
for hate speech detection are continuously being developed, there is little research on the …
Tweeteval: Unified benchmark and comparative evaluation for tweet classification
F Barbieri, J Camacho-Collados, L Neves… - arXiv preprint arXiv …, 2020 - arxiv.org
The experimental landscape in natural language processing for social media is too
fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics …
fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics …
TimeLMs: Diachronic language models from Twitter
Despite its importance, the time variable has been largely neglected in the NLP and
language model literature. In this paper, we present TimeLMs, a set of language models …
language model literature. In this paper, we present TimeLMs, a set of language models …
Ext5: Towards extreme multi-task scaling for transfer learning
Despite the recent success of multi-task learning and transfer learning for natural language
processing (NLP), few works have systematically studied the effect of scaling up the number …
processing (NLP), few works have systematically studied the effect of scaling up the number …
Hatebert: Retraining bert for abusive language detection in english
In this paper, we introduce HateBERT, a re-trained BERT model for abusive language
detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit …
detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit …
SemEval-2020 task 12: Multilingual offensive language identification in social media (OffensEval 2020)
We present the results and main findings of SemEval-2020 Task 12 on Multilingual
Offensive Language Identification in Social Media (OffensEval 2020). The task involves …
Offensive Language Identification in Social Media (OffensEval 2020). The task involves …