https://github.com/manideep2510/siamese-BERT-fake-news-detection-LIAR
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Fake News Detection is a natural language processing task that involves identifying and classifying news articles or other types of text as real or fake. The goal of fake news detection is to develop algorithms that can automatically identify and flag fake news articles, which can be used to combat misinformation and promote the dissemination of accurate information.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset
Fake News Detection on Social Media: A Data Mining Perspective
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network
A Survey on Natural Language Processing for Fake News Detection
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
TieFake: Title-Text Similarity and Emotion-Aware Fake News Detection
Fake News Detection on Social Media: A Data Mining Perspective
Deep Two-path Semi-supervised Learning for Fake News Detection
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.