https://github.com/blakzaei/NOL-GAT
공식 코드Neighborhood-Order Learning Graph Attention Network for Fake News Detection
Papers with Code 아카이브
Fake News Detection
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/blakzaei/dhgat
공식 코드A Decision-Based Heterogenous Graph Attention Network for Multi-Class Fake News Detection
https://github.com/brunocroso/contextualgnns-fakenews
공식 코드https://github.com/casperhansen/fake-news-reasoning
공식 코드Automatic Fake News Detection: Are Models Learning to Reason?
https://github.com/causalnlp/censorship
공식 코드Revealing Hidden Mechanisms of Cross-Country Content Moderation with Natural Language Processing
https://github.com/cgao-comp/GAMC
공식 코드GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking
https://github.com/chengxuphd/dcr
공식 코드https://github.com/chenxwh/fake-news-detection
공식 코드Evaluation of Fake News Detection with Knowledge-Enhanced Language Models
https://github.com/chunyuanY/FakeNewsDetection
공식 코드https://github.com/cipriantruica/misrobaerta_transformers-vs-misinformation
공식 코드https://github.com/cleopatra-itn/image_text_claim_detection
공식 코드https://github.com/clickbait-challenge/snapper
공식 코드A Two-Level Classification Approach for Detecting Clickbait Posts using Text-Based Features
https://github.com/code4graph/hero
공식 코드Linguistic-style-aware Neural Networks for Fake News Detection
https://github.com/cripac-dig/getral
공식 코드Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks
https://github.com/david-yoon/detecting-incongruity
공식 코드Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder
https://github.com/deu30303/AdStyle
공식 코드Adversarial Style Augmentation via Large Language Model for Robust Fake News Detection
https://github.com/devank3/CoVLM
공식 코드https://github.com/diego-feijo/bertpt
공식 코드Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks
https://github.com/diptamath/covid_fake_news
공식 코드A Heuristic-driven Ensemble Framework for COVID-19 Fake News Detection
https://github.com/dogregor/graph-fakenewsnet
공식 코드Exploring Fake News Detection with Heterogeneous Social Media Context Graphs
https://github.com/dogregor/pretrain_gnns_fakenewsnet
공식 코드https://github.com/dongqifu/disco
공식 코드DISCO: Comprehensive and Explainable Disinformation Detection
https://github.com/draip96/fakenewsclassification
공식 코드https://github.com/ds4ai-upb/danes
공식 코드https://github.com/dsfsi/sa-fake-news-2020
공식 코드Is it Fake? News Disinformation Detection on South African News Websites
https://github.com/enderfga/doublecheck
공식 코드https://github.com/entitize/fakeddit
공식 코드r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
https://github.com/everythingguy/covidmis20
공식 코드CovidMis20: COVID-19 Misinformation Detection System on Twitter Tweets using Deep Learning Models
https://github.com/fakenewsresearch/dataset
공식 코드https://github.com/gabll/some-like-it-hoax
공식 코드Some Like it Hoax: Automated Fake News Detection in Social Networks
https://github.com/hamidkarimi/DHSF
공식 코드Learning Hierarchical Discourse-level Structure for Fake News Detection
https://github.com/hamidkarimi/HDSF
공식 코드Learning Hierarchical Discourse-level Structure for Fake News Detection
https://github.com/hansd410/cross-lingfnd
공식 코드Cross-lingual Transfer Learning for Fake News Detector in a Low-Resource Language
https://github.com/hettransformer/hettransformer-model
공식 코드https://github.com/hlr/misinformation-detection
공식 코드Using Persuasive Writing Strategies to Explain and Detect Health Misinformation
https://github.com/iandreafc/gdeltnews
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.