Model for Fake News Detection Using AI Technique
Bibliographic record
Abstract
The spread of fake news on social media platforms can have serious consequences for society, especially in urgent situations such as crises.Despite efforts to combat it, fake news is still able to proliferate rapidly through social media, where users share and exchange a vast amount of information on a daily basis.This information, however, is not always accurate, making it difficult to distinguish real news from fake news.To address this problem, this research proposes additional characteristics based on social interactions and content to identify fake news on social media platforms.These characteristics are designed to work in conjunction with each other and are found to be more effective in identifying fake news compared to the current baseline criteria.In addition, a CNN-LSTM model is used to analyze the text and predict the veracity of news.Unlike early research that focuses on fake news that has been circulating for a long time, this study tests the identification of fake news on a real-world dataset.The proposed features and machine learning models outperformed the baseline in terms of accuracy, recall, and F1 metrics, which are standard measures of classification model performance.
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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 | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".