Fake News Detection Using Weighted Fine-Tuned BERT and Sparse Recurrent Neural Network
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
Fake news refers to misinformation or false reports shared in the form of images, articles, or videos, disguised as real news to manipulate people’s opinions. Recently, fake news and rumors have spread extensively and rapidly around the world. This has led to the production and propagation of inaccurate news articles. Therefore, it is necessary to restrict the spread of fake information in the media to establish confidence globally. For this purpose, this research proposes Weighted Fine-tuned-Bidirectional Encoder Representations from Transformers-based Sparse Recurrent Neural Network (WFT-BERT-SRNN) for fake news detection through Deep Learning (DL). Data preprocessing is established using stop word removal, tokenization, and stemming to eliminate unwanted phrases or words. Then, WFT-BERT is employed for feature extraction, and finally, SRNN is employed to detect and classify fake news as real or fake. WFT-BERT-SRNN achieves a superior accuracy of 0.9847, 0.9724, 0.9624, and 0.9725 on the BuzzFeed, PolitiFact, Fakeddit, and Weibo datasets compared to existing techniques like DeepFake and image caption-based technique.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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