A Content-Based Chinese Spam Detection Method Using a Capsule Network With Long-Short Attention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most existing Chinese spam detection models suffer such problems as inaccurate representation, unsatisfactory detection effect and poor practicality. To address these problems, a capsule network model combining the long-short attention mechanism is proposed here to achieve efficient Chinese spam detection. For text representation, the proposed model uses a multi-channel structure based on the long-short attention mechanism, which can capture complex text features in spam and generate contextual word vectors with more semantic information. For feature mining and classification, the model improves the structure of the traditional capsule network without compromising the classification performance and optimizes the dynamic routing algorithm, so that the model has a high accuracy without reducing the running speed. Experimental results show that the model outperformed the current mainstream methods such as TextCNN, LSTM and even BERT in characterization and detection; and it achieved an accuracy as high as 98.72% on an unbalanced dataset and 99.30% on a balanced dataset.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 it