Multi-Modal CSNNs for Integrated Toxicity Detection Across Text, Audio, and Visual Modalities
Bibliographic record
Abstract
Online toxicity has become increasingly prevalent, spanning various communication channels such as text, audio, and visual content. Traditional single-modality approaches often fail to capture the nuanced and complex nature of toxic behavior, leading to incomplete or inaccurate detection. In this paper, we present a novel multi-modal approach for detecting online toxicity using a Convolutional Spiking Neural Network (CSNN). Our framework integrates text embeddings, mel spectrogram representations of audio, and visual data, enabling a comprehensive analysis of toxic interactions. By leveraging the unique temporal and spatial capabilities of CSNNs, our model effectively fuses multi-modal features through a late fusion strategy, improving accuracy and robustness in toxicity classification tasks. Experimental results demonstrate the superior performance of our approach on benchmark datasets, highlighting its ability to capture subtle and context-specific toxic behaviors across multiple data streams. This work lays the groundwork for more sophisticated multi-modal systems capable of addressing the complex challenges of online toxicity detection.
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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 | 0.000 | 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".