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Multilingual Stance Detection in Tweets: The Catalonia Independence Corpus

Summary

Automatic stance detection, which classifies whether a text supports or opposes a given target, has mostly been studied in English because labelled data in other languages is scarce. The earlier Catalan/Spanish resource from IberEval 2018, the TW-10 Referendum Dataset, has a heavily skewed label distribution, especially in its Catalan portion. This makes cross-language comparisons of systems unreliable. To fix this, the authors built the Catalonia Independence Corpus (CIC), a class-balanced set of Catalan and Spanish tweets labelled for stance on Catalan independence. Instead of labelling every tweet by hand, they used a semi-automatic procedure that assigns labels based on a categorization of Twitter users. Both corpora were used to benchmark four supervised systems, ranging from linear SVMs to neural text classifiers, with shared normalization and dictionary-based lemmatization. The comparison shows the value of balanced data for multilingual and cross-lingual stance research. The authors also report new state-of-the-art scores on TW-10 for both languages. All data and code are publicly released.

Key points

  • Resource: CIC covers Catalan and Spanish tweets with a single target, the independence of Catalonia. It is released with code at github.com/ixa-ehu/catalonia-independence-corpus.
  • Annotation: Tweet labels are derived semi-automatically from a categorization of Twitter users, which reduces manual tweet-by-tweet labelling.
  • Systems tested:
    • SVM + TF-IDF
    • SVM over fastText embeddings
    • the fastText classifier
    • Flair (an RNN with static and contextual string embeddings)
  • Preprocessing: Removal of mentions, URLs and numbers, spelling normalization, diacritic stripping, dictionary-lookup lemmatization and stopword removal.
  • Prior TW-10 bests: uc3m reached 28.02 macro-F1 for Spanish with TF-IDF bag-of-words. CriCa reached 30.68 for Catalan by merging both languages and applying stemming. The provided extract omits CIC's size and the new TW-10 scores; see the full paper for these figures.

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

Cited by

26 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 22 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{zotova2020multilingual,
  title = {{Multilingual Stance Detection in Tweets: The Catalonia Independence Corpus}},
  author = {Zotova, Elena and Agerri, Rodrigo and Nuñez, Manuel and Rigau, German},
  booktitle = {Proceedings of the Twelfth Language Resources and Evaluation Conference},
  year = {2020},
  url = {https://aclanthology.org/2020.lrec-1.171/}
}

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