15 papers with code • 3 benchmarks • 6 datasets
The proliferation of fake news, i. e., news intentionally spread for misinformation, poses a threat to individuals and society.
This paper releases "AraCOVID19-MFH" a manually annotated multi-label Arabic COVID-19 fake news and hate speech detection dataset.
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.
We present LSTM-Shuttle, which applies human speed reading techniques to natural language processing tasks for accurate and efficient comprehension.
Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification
The rising growth of fake news and misleading information through online media outlets demands an automatic method for detecting such news articles.
Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction.
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
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).
This work empirically demonstrates the ability of Text Graph Convolutional Network (Text GCN) to outperform traditional natural language processing benchmarks for the task of semi-supervised Swahili news classification.