Thou shalt not hate: Countering Online Hate Speech
이 논문의 초록은 아카이브 스냅샷(papers 덤프)에 포함되어 있지 않습니다. 아래의 원문·코드 링크를 이용하세요.
Code (2)
Tasks
Counterspeech DetectionResults from the Paper
| Rank | Task | Dataset | Model | Metrics |
|---|---|---|---|---|
| #1 | Counterspeech Detection | Youtube counterspeech dataset | XGBoost | F1 score: 0.715 |
Similar Papers 제목 키워드 기반
Countering Online Hate Speech: An NLP Perspective
Online hate speech has caught everyone's attention from the news related to the COVID-19 pandemic, US elections, and worldwide protests. Online toxicity - an umbrella term for online hateful behavior, manifests itself in…
Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language
Counterspeech, i.e., responses to counteract potential harms of hateful speech, has become an increasingly popular solution to address online hate speech without censorship. However, properly countering hateful language …
PhilosophyA Benchmark Dataset for Learning to Intervene in Online Hate Speech
Countering online hate speech is a critical yet challenging task, but one which can be aided by the use of Natural Language Processing (NLP) techniques. Previous research has primarily focused on the development of NLP m…
Response GenerationTowards Automatic Generation of Messages Countering Online Hate Speech and Microaggressions
With the widespread use of social media, online hate is increasing, and microaggressions are receiving attention. We explore the potential for using pretrained language models to automatically generate messages that comb…
InformativenessHuman-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering
Fighting online hate speech is a challenge that is usually addressed using Natural Language Processing via automatic detection and removal of hate content. Besides this approach, counter narratives have emerged as an eff…
Text Generation