Ensemble-Based Machine Learning Approach for Detecting Arabic Fake News on Twitter
Bibliographic record
Abstract
The rise of social media platforms has led to a significant increase in the spread of false or misleading information, which has become a major issue of concern.Twitter faces the difficult task of identifying and reducing the spread of 'fake news', which refers to material that is erroneously or intentionally spread.This type of content frequently includes false information, biased information, and data that is provided without considering its full context.The swift and comprehensive proliferation of platforms such as Twitter worsens the problem by enabling the widespread and rapid dissemination of unverified content, often leading to its viral dissemination and contributing to the propagation of falsehoods.This paper presents a specialized machine learning approach that utilizes an ensemblebased strategy to identify and classify false information on the social media platform Twitter.This approach utilizes the combined power of various classifiers, such as Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Gradient Boosting (GBoost), to create a strong prediction model by combining multiple weaker learners.Every classifier undergoes rigorous training using a specific set of variables, including text content, user profile information, and tweet metadata.This enables a thorough analysis to identify fake news.Within the proposed system, following separate training, the classifiers generate predictions that are then merged.A neural network is utilized to combine the outputs of all classifiers, resulting in a definitive prediction.This approach tackles the drawbacks of overfitting and improves the capacity of the model to apply to new data, resulting in a higher level of accuracy for the machine learning model.The empirical assessments conducted on a dataset that is freely accessible, containing both genuine and counterfeit tweets, show that the ensemble model performs much better than the individual base classifiers and traditional machine learning models.The proposed method attained an accuracy of 0.963, along with an Area Under the Curve (AUC) of 0.964, surpassing the precision, recall, and F1 scores of its individual classifiers.The results confirm the efficacy of the ensemble machine learning architecture as a dependable method for identifying false information in Arabic on Twitter.This has implications for wider usage on different social media platforms.
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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 | 0.003 |
| 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 | 0.000 |
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 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".