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Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model

Summary

The paper looks at how to automatically read customer opinion about Saudi Telecom Company (STC) on Twitter. Customers post complaints and feedback there in large volumes and in many Arabic dialects, so manual review is not realistic. The authors fine-tune MARBERT, a BERT-style model pre-trained on Arabic tweets in both dialectal Arabic and MSA, for two tasks. The first is spam detection. The second is five-way sentiment classification into positive, negative, neutral, sarcasm and indeterminate. The sentiment corpus has about 24.5K tweets and is heavily skewed toward negative posts. Rather than resampling, the authors gathered additional minority-class tweets, then swept batch size, learning rate, sequence length and epoch count. Spam detection reaches an F1 of 0.98, which beats the SVM and BERT baselines they cite. The best overall sentiment F1 is 0.75, and sarcasm remains the weakest class. The study shows that a tweet-specific Arabic encoder can handle noisy, imbalanced, multi-class customer data reasonably well.

Key points

  • The dataset has 24,513 STC-related tweets: 13,828 negative, 5,694 neutral, 2,297 indeterminate, 1,437 positive and 1,221 sarcasm.

  • MARBERT (Abdul-Mageed et al.) was pre-trained on 15.6B tokens of DA and MSA tweets. It omits the NSP objective because tweets are short.

  • On spam detection, MARBERT scores F1 0.98. The cited baselines are an SVM at 0.93 (Saab et al.) and a BERT-based model at 0.96 (Yaseen).

  • The best sentiment setup reaches overall F1 0.75. It uses train batch 96, eval batch 32, learning rate 2e-5, 5 epochs and a longer max sequence length (256 in the table).

  • Per-class F1 improved when moving from batch 16 to batch 96:

    ClassBatch 16Batch 96
    Positive0.850.92
    Neutral0.790.89
    Negative0.800.85
    Indeterminate0.500.56
    Sarcasm0.330.54

Interesting ideas

  • I1. The authors fix weak minority classes by gathering more real examples instead of oversampling or undersampling. Sarcasm grew from about 200 to about 1,400 tweets, and the scores rose; this avoids duplicated noise and discarded data. — For Mova (learn): When silver data is thin for rare constructions, mine more genuine instances for them rather than reweighting or copying the few already present.
  • I2. The label set includes an "indeterminate" class alongside "neutral". This keeps unclear tweets from being forced into a polarity label and contaminating the other classes. — For Mova (frames): Speech-act and irony labels in prag could include an explicit "undecidable" outcome, so uncertain readings are recorded honestly rather than guessed.
  • I3. Results are reported per class, which shows sarcasm lagging far behind the overall score. A single aggregate F1 would have hidden that gap. — For Mova (learn): Evaluation reports should break scores down by phenomenon (e.g., irony, rare relations), so averages over frequent cases do not mask failures.
  • I4. The pre-training objective is matched to the text genre: MARBERT drops NSP because tweets are too short for sentence-pair learning. — For Mova (tree): When adapting gparse to short or fragmentary inputs such as headlines or dialogue turns, drop training signals that assume multi-sentence context.
  • I5. Hyperparameter runs are laid out as a sweep table, so the effect of each setting can be traced. — For Mova (learn): Keep a similar per-run log for parser and tagger training, so every configuration choice stays explainable and reproducible.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18280/mmep.090617 · arXiv 2606.25495

@misc{alotaibi2026spam,
  title = {{Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model}},
  author = {Alotaibi, Abrar and Rahman, Atta-ur and Alhaza, Raheel and Alkhalifa, Wala and Alhajjaj, Narjes and Alharthi, Atheer and Abushoumi, Dhai and Alqahtani, Maryam and Alkhulaifi, Dania},
  year = {2026},
  doi = {10.18280/mmep.090617},
  eprint = {2606.25495},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18280/mmep.090617}
}

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