Detection of COVID-19 Fake News in Online Social Networks with the Developed CNN-LSTM Based Hybrid Model
Bibliographic record
Abstract
Technological developments have led to the emergence of different platforms. Social media platforms are one of the most used platforms recently. In this study, a text-based study was conducted on fake news sharing about COVID-19 in online social networks with Shallow Learning (SL) and Deep Learning (DL) methods. In order to classify the news in the dataset, the news in the dataset is converted into a format that can be understood by the machines in the preprocessing step. In the study, the glove method was used for word representation. The document matrix obtained using the glove method was classified with the proposed hybrid model. In the proposed hybrid model, LSTM and CNN structures are used together. In addition, different Shallow Learning methods accepted in the literature were used to compare the performances of the proposed model, and the results were obtained and these results were compared with the proposed model. Among these models, the most successful results were obtained in the proposed hybrid model. When the performance evaluation metrics obtained are examined, it is obvious that the proposed model can be used to solve many other social media and network problems related to COVID-19.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".