Semisupervised Federated Learning for Temporal News Hyperpatism Detection
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".