A Machine Learning Framework for Automatic Fake News Detection in Indian Tamil News Channels
Bibliographic record
Abstract
With the development of technology and social media, many people started using the Internet.Today, everyone is creating and sharing content on social media.Before they transfer it to others, no one checks the originality of the content; it is com manually identifies the news content as fake or real manually.Due to this challenge, many people are started sharing fake news purposely to destroy the community, and some political and business purposes are spreading quickly.News channels and online newspapers have challenges in identifying trustworthy news sources.In this research paper, we collected various news articles from Indian news are gathered and will perform preprocessing, feature extraction, classification and prediction are going to be done using Naï ve Bayes, Logistic Regression and LSTM.The proposed approach has 35,550 trustworthy news, and 15,450 fake news and TF-IDF techniques are used for the feature extraction.These three proposed algorithms are going to compare and predict the results.The Long Short-Term Memory will detect the accuracy of fake news in the Indian news channel is 99.7%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".