Electoral Agitation Data Set: The Use Case of the Polish Election
This addresses a niche problem for election administrators in Poland by enabling automated detection of political agitation, though it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of tracking electoral agitation on social media by creating the first publicly available dataset of 6,112 annotated Polish tweets, achieving a 0.66 inter-annotator agreement and fine-tuning a model to a 68% F1 score.
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.