A Deep Learning and AutoML-Based Multimodal Text Extraction Framework for Detecting Online Gambling Advertisements in Indonesian Social Media
Bibliographic record
Abstract
The growing presence of online gambling advertisements on social media threatens digital security and complicates content moderation, particularly in multilingual and noisy environments such as Indonesian platforms.This study proposes a deep learning and AutoML-based multimodal text extraction framework that integrates textual posts, optical character recognition (OCR) from images, and automatic speech recognition (ASR) from videos for comprehensive gambling content detection.Four approaches were evaluated: Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Bidirectional Encoder Representations from Transformers (BERT), and AutoML (AutoGluon).Experiments were conducted under standardized preprocessing and hyperparameteroptimized conditions, with performance assessed using accuracy, precision, recall, and F1-score.The RNN achieved the best results (accuracy 93.07%, precision 93.22%, recall 92.87%, F1-score 93.04%), followed by CNN (accuracy 92.80%, F1-score 92.59%) and AutoML (accuracy 90.19%, F1-score 90.18%).BERT underperformed (accuracy 68.12%, F1-score 68.18%) due to limited domain adaptation to noisy Indonesian text.Error analysis revealed three major challenges: implicit promotional language, OCR/ASR transcription noise, and contextual ambiguity.These findings demonstrate the effectiveness of multimodal text integration combined with optimized deep learning models, offering a scalable solution for harmful content moderation and digital security in social media.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".