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Found 4 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.
Measuring vagueness and subjectivity in texts: from symbolic to neural VAGO
We present a hybrid approach to the automated measurement of vagueness and subjectivity in texts.
Semantic of Cloud Computing services for Time Series workflows
The processing and analysis of TS are essential in order to extract knowledge from the data and to tackle forecasting or predictive maintenance tasks among others The modeling of TS is a challenging task, requiring high statistical expertise as well as outstanding knowledge about the application of Data Mining(DM) and Machine Learning (ML) methods.
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.
