News Classification

24 papers with code • 3 benchmarks • 11 datasets

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Most implemented papers

Explainable Tsetlin Machine framework for fake news detection with credibility score assessment

cair/TsetlinMachine LREC 2022

The proliferation of fake news, i. e., news intentionally spread for misinformation, poses a threat to individuals and society.

AraCOVID19-MFH: Arabic COVID-19 Multi-label Fake News and Hate Speech Detection Dataset

MohamedHadjAmeur/AraCOVID19-MFH 7 May 2021

This paper releases "AraCOVID19-MFH" a manually annotated multi-label Arabic COVID-19 fake news and hate speech detection dataset.

Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles

bkolosk1/kbnr 20 Oct 2021

Increasing amounts of freely available data both in textual and relational form offers exploration of richer document representations, potentially improving the model performance and robustness.

A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification

ruixinhua/batm Findings (ACL) 2022

Many recent deep learning-based solutions have widely adopted the attention-based mechanism in various tasks of the NLP discipline.

Speed Reading: Learning to Read ForBackward via Shuttle

tsujuifu/pytorch_lstm-shuttle EMNLP 2018

We present LSTM-Shuttle, which applies human speed reading techniques to natural language processing tasks for accurate and efficient comprehension.

Fake News Detection as Natural Language Inference

zake7749/WSDM-Cup-2019 17 Jul 2019

The remainder of test cases are classified by our ensemble.

Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification

MysteryVaibhav/fake_news_semantics WS 2019

The rising growth of fake news and misleading information through online media outlets demands an automatic method for detecting such news articles.

Assessing Robustness of Text Classification through Maximal Safe Radius Computation

EmanueleLM/MCTS Findings of the Association for Computational Linguistics 2020

Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction.

KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi

Andrews2017/KINNEWS-and-KIRNEWS-Corpus COLING 2020

Recent progress in text classification has been focused on high-resource languages such as English and Chinese.

IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages

AI4Bharat/indic-bert Findings of the Association for Computational Linguistics 2020

These resources include: (a) large-scale sentence-level monolingual corpora, (b) pre-trained word embeddings, (c) pre-trained language models, and (d) multiple NLU evaluation datasets (IndicGLUE benchmark).