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Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic

Paper · 2022 · Firoj Alam, Hamdy Mubarak, Wajdi Zaghouani et al.

Summary

This paper reports on a shared task held at WANLP 2022 that aimed to bring propaganda-technique detection, previously studied almost only in English, to Arabic. The organizers built what they describe as the first Arabic dataset for this problem, focused on social media. It consists of tweets from major Arabic-language news outlets, annotated with a fine-grained inventory of persuasion techniques adapted from earlier English work on news articles and memes. The task had two parts. Subtask 1 is multilabel classification: say which techniques a tweet uses. Subtask 2 is sequence labeling: also mark the text spans where each technique occurs. Participation was strong at registration, though far fewer teams submitted results, especially for span detection. Most systems fine-tuned pre-trained Arabic transformers after routine text normalization. A few added data augmentation, ensembles or hand-written rules. The resource matters because it extends computational propaganda research to a major, under-served language, and it gives a shared benchmark for comparing approaches.

Key points

  • Data collection: The organizers used Social Bakers to pick the two leading news sources in each Arab country and added five international Arabic broadcasters (Al-Hurra, BBC Arabic, CNN Arabic, France 24, Russia Today). They pulled up to 3,200 recent tweets per source, drew 100 random tweets from each, and then sampled 930 tweets for annotation.
  • Label set: About 20 techniques were taken from Da San Martino et al. (2019b) and Dimitrov et al. (2021b). They include emotional appeals such as loaded language, flag-waving and appeal to fear, and logical fallacies such as whataboutism, straw man and red herring.
  • Participation: 63 teams registered. 14 submitted runs for Subtask 1 and 3 for Subtask 2, and 11 wrote system description papers.
  • Models used: Popular backbones were MARBERT/MARBERTv2, ARBERT, AraBERT variants, XLM-RoBERTa, AraELECTRA and DeHateBERT. The AraProp team found that MARBERTv2 gave the best micro-F1 among the eight models it tried.
  • Future plans: The organizers want a larger dataset and a hierarchical organization of the techniques.

Interesting ideas

  • I1. Sampling for topic balance: Taking a fixed number of random tweets per outlet, rather than pooling everything, keeps a few prolific sources from dominating the corpus. This is a cheap way to get topical and regional variety in any news-derived dataset.
  • I2. Mixing models with rules: One team combined a fine-tuned classifier with specialized components. A NER model handled "Name calling" (labels often attach to entities), and simple rules handled "Repetition," which is essentially a surface pattern. Assigning label types to whichever tool suits them best could help other multilabel tasks with heterogeneous classes.
  • I3. Tuning the decision threshold: One team set the multilabel cutoff at 0.32 rather than 0.5, after noticing a clear score gap on validation data. Inspecting the score distribution before choosing a threshold is a small change that may help in imbalanced multilabel settings.
  • I4. Nearest-neighbour label transfer: One baseline-like approach embedded sentences with BERT and found the five most similar training examples (cosine similarity above 0.4). It then assigned the three most frequent labels among them. This needs no fine-tuning, which makes it a useful low-resource fallback or sanity check.
  • I5. Augmentation by mixing sequences: One team created synthetic training examples by randomly recombining existing sequences, growing the data to about 2,000 examples. This is a simple way to enlarge very small annotated corpora, though its effect on label fidelity would need checking.

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

 Institutions

🎓Hamad bin Khalifa UniversityDoha · Qatar71 works · 12,942 citations🎓Mohamed bin Zayed University of Artificial IntelligenceAbu Dhabi · United Arab Emirates47 works · 4,456 citations

📚 Shelves 4 Computer Science · 9,086 works Artificial Intelligence · 8,889 works Social Sciences · 744 works Sociology and Political Science · 221 works

🧭 Topics 2 Evaluation & benchmarks · 685 works Semitic languages · 1,484 works

Cite

Cite as Firoj Alam, Hamdy Mubarak, Wajdi Zaghouani, Giovanni Da San Martino and Preslav Nakov. 2022. Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic. Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2022.wanlp-1.11

@inproceedings{alam2022overview,
  title = {{Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic}},
  author = {Firoj Alam, Hamdy Mubarak, Wajdi Zaghouani, Giovanni {Da San Martino} and Preslav Nakov},
  booktitle = {Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)},
  year = {2022},
  doi = {10.18653/v1/2022.wanlp-1.11},
  url = {https://doi.org/10.18653/v1/2022.wanlp-1.11}
}

References

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