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DataBees at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization

Paper · 2026 · Tanisha Sriram, Sathvika Shankar, Sowmya Anand et al.

Summary

The DataBees team took part in SemEval-2026 Task 9 (POLAR), Subtask 1. The task is binary detection of polarized opinion in online texts, such as news, Reddit, blogs, Bluesky and forums, in Arabic, Bengali, German, Italian and Spanish. Each language has roughly 3.2–3.4k training examples.

The team wanted to know when classical sparse models are enough and when pretrained transformers are worth using. They compared two families of models:

  • Classical baselines: Naive Bayes, Logistic Regression and Linear SVM classifiers built on TF-IDF features and trained on heavily normalized text.
  • Transformers: multilingual models (mBERT, XLM-R) and monolingual models (BETO, AraBERTv2, MARBERT, BanglaBERT, MuRIL, GermanBERT, GELECTRA, several Italian BERT/RoBERTa variants), fine-tuned on raw text.

No single approach won everywhere:

  • Language-specific transformers clearly led for Bengali and Arabic, and narrowly led for German and Italian.
  • For Spanish, a TF-IDF+SVM pipeline beat every transformer tried.

The authors attribute these differences to dataset size, morphological complexity and how well each model's pretraining domain matches the task data. Their practical message is that model choice should depend on the language and domain, not on architecture size alone.

Key points

  • Data: about 3,180–3,380 train, 159–169 dev and 1,432–1,538 test instances per language, each with a binary polarization label.

  • Best F1 per language:

    LanguageBest modelF1
    BengaliBanglaBERT0.814
    ArabicMARBERT0.763
    GermanGELECTRA0.666
    ItalianUmBERTo0.594
    SpanishTF-IDF+SVM0.689

    For Spanish, BETO and mBERT reached only about 0.58.

  • Multilingual models: XLM-R and mBERT were stable across languages but generally trailed the specialized monolingual models.

  • Preprocessing: text for the classical pipeline was lowercased, stripped of punctuation, stopwords and emoji, and stemmed or lemmatized. Transformers received the raw text so they could use surface cues.

  • Leaderboard ranks:

    • Bengali: 6/48
    • Italian: 6/43
    • Arabic: 33/44
    • German: 41/44
    • Spanish: 46/48

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

🎓Sri Sivasubramaniya Nadar College of EngineeringChennai · India10 works · 9 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Semitic languages · 2,908 worksMultilingual & cross-lingual NLP · 828 works

Cite

Cite as Tanisha Sriram, Sathvika Shankar, Sowmya Anand, Rajalakshmi Sivanaiah, Angel Deborah S and Mirnalinee Thankanadar. 2026. DataBees at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization. Proceedings of the 20th International Workshop on Semantic Evaluation (2026).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.semeval-1.268

@inproceedings{sriram2026databees,
  title = {{DataBees at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization}},
  author = {Tanisha Sriram, Sathvika Shankar, Sowmya Anand, Rajalakshmi Sivanaiah, Angel Deborah S and Mirnalinee Thankanadar},
  booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)},
  year = {2026},
  doi = {10.18653/v1/2026.semeval-1.268},
  url = {https://doi.org/10.18653/v1/2026.semeval-1.268}
}

References

10 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Wissam Antoun, Fady Baly, and Hazem Hajj. 2020. AraBERT: Transformer-based model for Arabic language understanding. In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, pages 9–15, Marseille, France. European Langu…
  2. Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad, Kazi Samin Mubasshir, Md Saiful Islam, Anindya Iqbal, M. Sohel Rahman, and Rifat Shahriyar. 2022. BanglaBERT: Language model pretraining and benchmarks for low-resource language understanding evaluation in Bangla. In Findings of the Association for Comp…
  3. Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association fo… find
  4. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,…
  5. F. Harrag, E. El-Qawasmeh, and P. Pichappan. 2009. Improving arabic text categorization using decision trees. In Proceedings of the First International Conference on Networked Digital Technologies, pages 110–115. find
  6. Saif M. Mohammad, Parinaz Sobhani, and Svetlana Kiritchenko. 2016. Stance and sentiment in tweets. Preprint, arXiv:1605.01655. find
  7. Usman Naseem, Robert Geislinger, Juan Ren, Sarah Kohail, Rudy Garrido Veliz, P Sam Sahil, Yiran Zhang, Marco Antonio Stranisci, Idris Abdulmumin, Özge Alacam, Cengiz Acartürk, Aisha Jabr, Saba Anwar, Abinew Ali Ayele, Simona Frenda, Alessandra Teresa Cignarella, Elena Tutubalina, Oleg Rogov, Aung Ky… — POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization
  8. Usman Naseem, Robert Geislinger, Juan Ren, Sarah Kohail, Rudy Garrido Veliz, P Sam Sahil, Yiran Zhang, Marco Antonio Stranisci, Idris Abdulmumin, Özge Alaçam, Cengiz Acartürk, Aisha Jabr, Saba Anwar, Abinew Ali Ayele, Elena Tutubalina, Aung Kyaw Htet, Xintong Wang, Surendrabikram Thapa, Tanmoy Chakr… — SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization
  9. Bo Pang and Lillian Lee. 2008. Opinion mining and sentiment analysis. Found. Trends Inf. Retr., 2(1–2):1–135. find
  10. F. Rollo, G. Bonisoli, and L. Po. 2024. A comparative analysis of word embeddings techniques for italian news categorization. IEEE Access, 12:25536–25552. find

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