Cross-lingual Transfer Learning for Detecting Negative Campaign in Israeli Municipal Elections: a Case Study

Natalia Vanetik, Marina Litvak, Lin Miao

Research output: Contribution to journalConference articlepeer-review


Political competitions are complex settings where candidates use campaigns to promote their chances to be elected. As we can recently observe, some candidates choose to focus on a negative campaign that emphasizes the negative aspects of the competing person and is aimed at offending opponents or the opponent's supporters. The big challenge in this area is the lack of annotated datasets for training efficient classifiers. Therefore, transfer learning from other relevant domains and other languages could be very useful for this task. Considering the recent success of meta-learning in domain adaptation, we apply it to our task of utilizing available datasets from different domains and languages. This work explores the negative campaign detection task from multiple perspectives: the efficiency of different text representations and classification models, and the effect of transfer learning from offensive language detection in different languages for negative campaign detection in Hebrew. We demonstrate that the lack of training data for negative campaign detection in a low-resourced language such as Hebrew can be compensated to some extent by available datasets for offensive language detection in the same and other languages. We report an empirical case study for political campaigns in Israeli municipal elections.

Original languageEnglish
Pages (from-to)83-92
Number of pages10
JournalCEUR Workshop Proceedings
StatePublished - 1 Jan 2023
Externally publishedYes
Event6th Workshop on Narrative Extraction From Texts, Text2Story 2023 - Dublin, Ireland
Duration: 2 Apr 2023 → …


  • BERT
  • Hebrew
  • meta-learning
  • negative campaign
  • text classification

ASJC Scopus subject areas

  • General Computer Science


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