Semisupervised Federated Learning for Temporal News Hyperpatism Detection

The proliferation of false and erroneous information on the Internet has posed a challenge to the accurate exchange of information. To address this issue, a semisupervised system based on self-embedding has been proposed. This system verifies information before it is shared, allowing only reliable a...

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Bibliographic Details
Published in:IEEE transactions on computational social systems Vol. 10; no. 4; pp. 1 - 12
Main Authors: Ahmed, Usman, Lin, Jerry Chun-Wei, Srivastava, Gautam
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Coders
Embedding
Federated learning
Federated learning (FL)
Labels
Machine learning
News
news articles
Performance enhancement
Predictions
Retrieval
Semi-supervised learning
semisupervised learning
social analysis
ISSN:2329-924X, 2373-7476
Online Access:Get full text
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Summary:The proliferation of false and erroneous information on the Internet has posed a challenge to the accurate exchange of information. To address this issue, a semisupervised system based on self-embedding has been proposed. This system verifies information before it is shared, allowing only reliable and accurate content to be disseminated and protecting individuals from the negative effects of false information. In this article, we present a news article retrieval model based on active learning (AL) in a semisupervised learning setting. This model has the advantages of limited communication requirements, strong scalability, increased data privacy, and a time-dependent retrieval model. We use lexicon expansion, content segmentation, and temporal events to generate a bidirectional encoder representations from transformer (BERT) attention embedding query for the temporal understanding of sequential news articles. To generate pseudo-labels, we combine the partially trained model with the original tagged data. An attention network is used to update pseudo-labels of data samples when the label of a sample is correctly or incorrectly predicted. Finally, the modified classifiers are combined to make predictions. Experimental results indicate that the proposed model has 81% performance, showing that co-training and semisupervised learning can improve the performance of temporal expansion and profiling algorithms.
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ISSN:2329-924X
2373-7476
DOI:10.1109/TCSS.2023.3247602