Accurate Fake News Detection in Text-Based Content Using K-Nearest Neighbor, LSTM, MLP and CNN Models
Bibliographic record
Abstract
With the proliferation of social media platforms, where users are free to express themselves and share content, the detection of fake news in text has become an important issue. The dissemination of inaccurate or incorrect information is made easier by this, even while it improves communication. The public's capacity to differentiate between genuine content and disinformation is further complicated by the accessibility of diverse internet news sources. Strong methods for detecting and reporting false news must be developed in order to solve this problem. They start by managing missing values, removing noise, tokenising, and stemming the dataset. Our focus is on COVID-19-related fake news in this study. We use the TF-IDF technique to extract features. To improve the identification of false news, we provide CNLSMLKN, a new hybrid model that integrates deep learning and machine learning techniques, including CNN, LSTM, MLP, and KNN. Despite its hybrid architecture's origins in solar irradiance data analysis, it works wonders when it comes to identifying false news. Our findings show that compared to traditional models, the suggested one obtains a far higher prediction accuracy of 98%. The results show that the approach could be useful in the fight against fake news online.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".