Political Advertising Dataset: the use case of the Polish 2020 Presidential Elections
This provides a resource for analyzing political campaigns in Polish, though it is incremental as it applies existing methods to a new language-specific dataset.
The authors tackled the lack of publicly available datasets for political advertising detection in Polish by creating a dataset of 1,705 annotated tweets with nine categories, achieving a 0.65 inter-annotator agreement and training a neural tagger that reached a 70% F1 score.
Political campaigns are full of political ads posted by candidates on social media. Political advertisements constitute a basic form of campaigning, subjected to various social requirements. We present the first publicly open dataset for detecting specific text chunks and categories of political advertising in the Polish language. It contains 1,705 human-annotated tweets tagged with nine categories, which constitute campaigning under Polish electoral law. We achieved a 0.65 inter-annotator agreement (Cohen's kappa score). An additional annotator resolved the mismatches between the first two annotators improving the consistency and complexity of the annotation process. We used the newly created dataset to train a well established neural tagger (achieving a 70% percent points F1 score). We also present a possible direction of use cases for such datasets and models with an initial analysis of the Polish 2020 Presidential Elections on Twitter.