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# Detection of Negative Campaign in Israeli Municipal Elections

**Authors:** [Marina Litvak](https://md.mova.elamur.ai/free/en/docs/papers/authors/marina-litvak/), [Natalia Vanetik](https://md.mova.elamur.ai/free/en/docs/papers/authors/natalia-vanetik/), Sagiv Talker, Or Machlouf · **Year:** 2022 · **Venue:** Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022) **Link:** [aclanthology.org/2022.trac-1.9](https://aclanthology.org/2022.trac-1.9/) · library `aclbulk-2022-litvak-detection-negative-campaign-israeli-municipal` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**0** citations](https://openalex.org/works?filter=cites:W6910652194) openalex-snapshot · 2026-10-14[**1** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2022-litvak-detection-negative-campaign-israeli-municipal/) of the 140673 papers whose bibliographies we read[Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022)](https://aclanthology.org/volumes/2022.trac-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2022.trac-1.9/)
- [PDF](https://aclanthology.org/2022.trac-1.9.pdf)
- [DOI 10.48448/rytw-2v80](https://doi.org/10.48448/rytw-2v80)
- [OpenAlex](https://openalex.org/W6910652194)

**Authors (4)**

1. Marina Litvak [Semantic Scholar](https://www.semanticscholar.org/author/143752322)
2. [Natalia Vanetik](https://openalex.org/A5080435987) [ORCID](https://orcid.org/0000-0002-4939-1415) · [Semantic Scholar](https://www.semanticscholar.org/author/2354144) · h 11 · 624 citations · 104 works
3. Sagiv Talker [Semantic Scholar](https://www.semanticscholar.org/author/2187455095)
4. Or Machlouf [Semantic Scholar](https://www.semanticscholar.org/author/2187454587)

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

The paper introduces TONIC, a dataset of Hebrew Facebook posts by 27 mayoral candidates in 12 Israeli cities (2018 municipal elections). Of 4,426 posts, 2,632 were campaign-related; 2,064 of these were labelled negative campaign (majority rate 0.784; annotator kappa 0.862). The authors compare traditional classifiers (random forest, logistic regression, XGBoost) on tf-idf, character n-grams, unigrams to trigrams and BERT vectors, with and without sentiment labels from HeBERT, against fine-tuned multilingual BERT and AlephBERT. The best model is logistic regression on unigrams with sentiment labels (F1 0.819, accuracy 0.884, test split 20%). Transfer from an offensive-language dataset (OLaH) lowers accuracy for every model, so the two tasks differ. Of 30 misclassified posts that were inspected, 23 errors came from the annotators' labels, and 22 of the 30 were false negatives.

## Ideas

- I1. Count-based baseline first: on about 2,600 posts, unigram counts plus a sentiment feature beat fine-tuned transformers. Before any Hebrew model is claimed to help, Mova needs this kind of baseline, and a sentiment score from a lexicon can be tested as one more feature ([9. Pragmatics and politeness](https://md.mova.elamur.ai/free/en/docs/languages/he/09-pragmatics-politeness/)).
- I2. Audit the gold labels: the authors found most of their errors were annotation errors, not model errors. Take a random sample of disagreements on any Hebrew test set, re-label them by hand, and report the change beside the score.
- I3. Negative about a person is not the same as offensive: the paper separates content that attacks an opponent from offensive language. A pragmatic rule can flag a post as attacking a named candidate (target present, not vulgar) without using the offence labels.

## For Mova

I1 and I3 go into [9. Pragmatics and politeness](https://md.mova.elamur.ai/free/en/docs/languages/he/09-pragmatics-politeness/) as tests. I2 is a rule for the test plan in [12. Mova and Modern Hebrew: where it stands, what it would need](https://md.mova.elamur.ai/free/en/docs/languages/he/12-mova/). The dataset is a candidate test set; its licence is CC BY 4.0 as given, and it is not used as training data for the release until its use is checked.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.48448/rytw-2v80](https://doi.org/10.48448/rytw-2v80)

```
@inproceedings{litvak2022detection,
  title = {{Detection of Negative Campaign in Israeli Municipal Elections}},
  author = {Litvak, Marina and Vanetik, Natalia and Talker, Sagiv and Machlouf, Or},
  booktitle = {Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022)},
  year = {2022},
  doi = {10.48448/rytw-2v80},
  url = {https://doi.org/10.48448/rytw-2v80}
}
```

- [← previous Automated Extraction of Sentencing Decisions from Court Cases in the Hebrew Language](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2021-wenger-automated-extraction-sentencing-decisions-court/)
- [next → Transformer-based Hebrew NLP models for Short Answer Scoring in Biology](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2023-schleifer-transformer-based-hebrew-nlp-models/)
- [↑ back to Modern Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/he/)

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