mlsanityData to 14 Oct 2026

Employing Sentence Space Embedding for Classification of Data Stream from Fake News Domain

arXiv 2407.10807 ↗First posted 15 Jul 2024cs.CL cs.LGcited by 0

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Abstract (arXiv)

Tabular data is considered the last unconquered castle of deep learning, yet the task of data stream classification is stated to be an equally important and demanding research area. Due to the temporal constraints, it is assumed that deep learning methods are not the optimal solution for application in this field. However, excluding the entire -- and prevalent -- group of methods seems rather rash given the progress that has been made in recent years in its development. For this reason, the following paper is the first to present an approach to natural language data stream classification using the sentence space method, which allows for encoding text into the form of a discrete digital signal. This allows the use of convolutional deep networks dedicated to image classification to solve the task of recognizing fake news based on text data. Based on the real-life Fakeddit dataset, the proposed approach was compared with state-of-the-art algorithms for data stream classification based on generalization ability and time complexity.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 48 references that are in the map. First posted on arXiv 15 Jul 2024.

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25 of 48 references.

References · 25

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13 more
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