https://github.com/nguyenvanhoang7398/FANG
공식 코드FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
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/nguyenvo09/EACL2021
공식 코드Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection
https://github.com/nguyenvo09/EMNLP2020
공식 코드Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News
https://github.com/nguyenvo09/LearningFromFactCheckers
공식 코드Learning from Fact-checkers: Analysis and Generation of Fact-checking Language
https://github.com/ni9elf/3han
공식 코드https://github.com/nicolaischneider/FakeNewsDetectionVulnerability
공식 코드How Vulnerable Are Automatic Fake News Detection Methods to Adversarial Attacks?
https://github.com/nicozwy/cofced
공식 코드A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection
https://github.com/ningljy/dtdbd
공식 코드Dual-Teacher De-biasing Distillation Framework for Multi-domain Fake News Detection
https://github.com/npurg/faknow
공식 코드https://github.com/oldaandozerskaya/covid_news
공식 코드https://github.com/omar-sharif03/CONSTRAINT-AAAI2021
공식 코드Combating Hostility: Covid-19 Fake News and Hostile Post Detection in Social Media
https://github.com/phhartl/lrec_2022
공식 코드Applying Automatic Text Summarization for Fake News Detection
https://github.com/phoenixsecularbird/imci
공식 코드IMCI: Integrate Multi-view Contextual Information for Fact Extraction and Verification
https://github.com/rawat-mrinal06/fake_news
공식 코드https://github.com/rezacsedu/multimodal-hate-bengali
공식 코드Multimodal Hate Speech Detection from Bengali Memes and Texts
https://github.com/rowanz/grover
공식 코드https://github.com/rshaojimmy/multimodal-deepfake
공식 코드https://github.com/rupakdas18/fake-news-detection-after-llm-laundering
공식 코드Fake News Detection After LLM Laundering: Measurement and Explanation
https://github.com/rutvikvijjali/COVID-19-Claims-Dataset
공식 코드Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking
https://github.com/s-nlp/multilingual-fake-news
공식 코드https://github.com/safe-graph/GNN-FakeNews
공식 코드https://github.com/sanxing-chen/adv-fake
공식 코드https://github.com/scviab/dpod
공식 코드https://github.com/shaanchandra/SAFER
공식 코드Graph-based Modeling of Online Communities for Fake News Detection
https://github.com/shafna81/fakenewsdetection
공식 코드Performance Analysis of Transformer Based Models (BERT, ALBERT and RoBERTa) in Fake News Detection
https://github.com/sheetal83/ax-to-grind-urdu-dataset
공식 코드Ax-to-Grind Urdu: Benchmark Dataset for Urdu Fake News Detection
https://github.com/sheikhazhanmohammed/AAAI-Constraint-Shared-Tasks-2021
공식 코드LaDiff ULMFiT: A Layer Differentiated training approach for ULMFiT
https://github.com/shibu4064/indonlp
공식 코드From Scarcity to Capability: Empowering Fake News Detection in Low-Resource Languages with LLMs
https://github.com/singh-l/FNDLVS
공식 코드Fake News Detection: a comparison between available Deep Learning techniques in vector space
https://github.com/singh-l/hybrrid_fn_dat_
공식 코드https://github.com/sinhanyang/dual-can
공식 코드Entity-Aware Dual Co-Attention Network for Fake News Detection
https://github.com/slz0925/gamed
공식 코드GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection
https://github.com/suxinqi/daad
공식 코드DAAD: Dynamic Analysis and Adaptive Discriminator for Fake News Detection
https://github.com/talreiss/factor
공식 코드https://github.com/tibhannover/mm-fakenews-detection
공식 코드https://github.com/tibhannover/mm_claims
공식 코드MM-Claims: A Dataset for Multimodal Claim Detection in Social Media
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.