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Found 5 papers, 1 papers with code
A Multi-Label Dataset of French Fake News: Human and Machine Insights
We present a corpus of 100 documents, OBSINFOX, selected from 17 sources of French press considered unreliable by expert agencies, annotated using 11 labels by 8 annotators.
Exposing propaganda: an analysis of stylistic cues comparing human annotations and machine classification
This paper investigates the language of propaganda and its stylistic features.
Duplicate Detection in a Knowledge Base with PIKA
This paper explores the use of Graph Neural Network models producing node embeddings, in order to solve the not fully addressed problem of detecting similar items stored in a knowledge base.
Combining Vagueness Detection with Deep Learning to Identify Fake News
In this paper, we combine two independent detection methods for identifying fake news: the algorithm VAGO uses semantic rules combined with NLP techniques to measure vagueness and subjectivity in texts, while the classifier FAKE-CLF relies on Convolutional Neural Network classification and supervised deep learning to classify texts as biased or legitimate.
Arabizi Language Models for Sentiment Analysis
We show that a language model (BAERT) pre-trained on a large corpus (LAD) in the same language (Arabizi) as that of the fine-tuning dataset (SALAD), outperforms a state-of-the-art multi-lingual pretrained model (multilingual BERT) on a sentiment analysis task.
