Electoral Agitation Data Set: The Use Case of the Polish Election

TL;DR

This paper introduces the first publicly available Polish electoral agitation tweet dataset, annotated for legal categories, and demonstrates its use in fine-tuning a language model to detect electioneering messages.

Contribution

It provides a novel annotated dataset for electoral agitation detection in Polish and applies it to fine-tune a language model, enabling better monitoring of election-related social media content.

Findings

Achieved 0.66 inter-annotator agreement

Fine-tuned HerBERT with 68% F1 score

Analyzed Polish 2020 Presidential Election tweets

Abstract

The popularity of social media makes politicians use it for political advertisement. Therefore, social media is full of electoral agitation (electioneering), especially during the election campaigns. The election administration cannot track the spread and quantity of messages that count as agitation under the election code. It addresses a crucial problem, while also uncovering a niche that has not been effectively targeted so far. Hence, we present the first publicly open data set for detecting electoral agitation in the Polish language. It contains 6,112 human-annotated tweets tagged with four legally conditioned categories. We achieved a 0.66 inter-annotator agreement (Cohen's kappa score). An additional annotator resolved the mismatches between the first two improving the consistency and complexity of the annotation process. The newly created data set was used to fine-tune a Polish Language Model called HerBERT (achieving a 68% F1 score). We also present a number of potential use cases for such data sets and models, enriching the paper with an analysis of the Polish 2020 Presidential Election on Twitter.