@inproceedings{alabdullah-etal-2026-towards,
    title = "Towards More Transparent Online Campaigning: Detecting Political Campaign Content in Election-related Social Media Posts",
    author = "Alabdullah, Abdullah  and
      Gaughan, Conor  and
      Flavel, Thomas  and
      Varma, Shubhanjay  and
      Gibson, Rachel  and
      Cantijoch, Marta  and
      Cernat, Alexandru  and
      Batista-Navarro, Riza",
    editor = "Card, Dallas  and
      Field, Anjalie  and
      Keith, Katherine  and
      Mendelsohn, Julia",
    booktitle = "Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science",
    month = jul,
    year = "2026",
    address = "San Diego",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.nlpcss-1.15/",
    pages = "249--270",
    ISBN = "979-8-89176-426-2",
    abstract = "A large part of political campaigns during elections is now being conducted online, with political actors leveraging their networks on social media platforms. To maintain transparency in political communications, regulations applicable to online campaigning have been put in place in many democracies. While it should be straightforward for voters to determine who produced and funded online advertisements comprising paid political campaigns, it is much more challenging to detect if organic content, i.e., social media posts, pertains to political campaigning, due to possibly subtle yet suggestive language that can be used by certain actors. In this paper, we investigate the feasibility of automatically detecting whether a given tweet posted by a political actor pertains to political campaigning, and if yes, whether it was conveyed in a direct or indirect (subtle) manner. After establishing an annotation scheme for the task of detecting political campaign content in tweets, we fine-tuned three encoder models (BERT, BERTweet and PoliBERTweet) for the same task and evaluated their performance. Our results show that fine-tuning BERTweet leads to the best macro-averaged F1-score (0.776), although all models consistently struggle to detect indirect campaigning."
}