Semisupervised Federated Learning for Temporal News Hyperpatism Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it