Tackling toxicity in Arabic social media through advanced detection techniques
Bibliographic record
Abstract
Online social networks are currently the most widely utilized interactive media for interpersonal communication, emotional expression, and information sharing. Despite the helpful and fascinating content, unfortunately, inappropriate or abusive content, such as toxicity, hate speech, and insults, can occasionally be shared on social networks. Any kind of online abuse, including but not limited to cyberbullying, discrimination, abusive language, profanity, flames, hate speech, and harassment, is considered toxic content. While there has been little effort in the Arabic language, the majority of toxicity detection attempts have focused on English text. In this work, we constructed a standard Arabic dataset that can be used for toxicity and abuse detection on OSNs. The proposed dataset has been annotated by the experts of five native and fluent Arabic speakers and linguists. To evaluate the performance of our dataset, we conducted a series of experiments by using sixteen machine learning algorithms, the FastText model, and seven transfer learning architectures to compare the performance. Furthermore, we used four word embedding techniques (bag of words (BOW), term frequency-inverse document frequency (TF-IDF), FASTTEXT, and bidirectional encoder representations from transformers (BERT)). Our experimental results demonstrated that the fine-tuned MARBERTv2 model with BERT embedding outperforms the other models, achieving an F1-score of 92.43% and an accuracy of 92.21%. Notably, this study highlights the importance of addressing toxicity on social media platforms, considering diverse languages and cultures. This signifies a significant breakthrough in the classification of toxic tweets in Arabic.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".