@inproceedings{evgrafova-etal-2024-analysing,
    title = "Analysing Pathos in User-Generated Argumentative Text",
    author = "Evgrafova, Natalia  and
      Hoste, Veronique  and
      Lefever, Els",
    editor = "Afli, Haithem  and
      Bouamor, Houda  and
      Casagran, Cristina Blasi  and
      Ghannay, Sahar",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Political Sciences @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.politicalnlp-1.5",
    pages = "39--44",
    abstract = "While persuasion has been extensively examined in the context of politicians{'} speeches, there exists a notable gap in the understanding of the pathos role in user-generated argumentation. This paper presents an exploratory study into the pathos dimension of user-generated arguments and formulates ideas on how pathos could be incorporated in argument mining. Using existing sentiment and emotion detection tools, this research aims to obtain insights into the role of emotion in argumentative public discussion on controversial topics, explores the connection between sentiment and stance, and detects frequent emotion-related words for a given topic.",
}

While persuasion has been extensively examined in the context of politicians{'} speeches, there exists a notable gap in the understanding of the pathos role in user-generated argumentation. This paper presents an exploratory study into the pathos dimension of user-generated arguments and formulates ideas on how pathos could be incorporated in argument mining. Using existing sentiment and emotion detection tools, this research aims to obtain insights into the role of emotion in argumentative public discussion on controversial topics, explores the connection between sentiment and stance, and detects frequent emotion-related words for a given topic.
Evgrafova, Natalia Hoste, Veronique Lefever, Els
Analysing Pathos in User-Generated Argumentative Text