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- W3198338285 abstract "Understanding the phenomenon of disinformation and its spread through the internet has been an increasingly challenging task, but it is necessary since the effects of this type of content have their impacts in the most diverse areas and generate more and more impacts within society. Automated fact-checking systems have been proposed by applying supervised machine learning techniques to assist in filtering fake news. However, two challenges are still present, the first related to understanding disinformation in its subgroups. The second challenge is related to the availability of datasets containing news classified between true and false. This article proposes an exploratory analysis through unsupervised algorithms and the t-SNE technique to visualize data with high dimensionality, identify the subgroups present in the disinformation, and the identification of possible outsiders between the classes. We also propose a new Corpus in Portuguese containing 19446 news, classified as true and false, and 15 linguistic features extracted from this dataset. Finally, we propose to use two classification models using the Random Forest techniques, with and without intruders. In the end, the model without intruder achieved superior performance, reaching an accuracy of 97.33%." @default.
- W3198338285 created "2021-09-13" @default.
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- W3198338285 date "2021-01-01" @default.
- W3198338285 modified "2023-09-26" @default.
- W3198338285 title "An Exploratory Analysis on a Disinformation Dataset" @default.
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- W3198338285 doi "https://doi.org/10.1007/978-3-030-85672-4_11" @default.
- W3198338285 hasPublicationYear "2021" @default.
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