@InProceedings{Brenneis2020c,
    author = "Brenneis, Markus and Mauve, Martin",
    editor = "Fazzinga, Bettina and Furfaro, Filippo and Parisi, Francesco",
    title = "{Do I Argue Like Them? A Human Baseline for Comparing Attitudes in Argumentations}",
    abstract = "When different persons exchange their attitudes in an argumentation, it is useful to measure how similar the attitudes in that argumentation are: for distance-based recommender systems which suggest other arguments, finding clusters of similar people, or comparing one's own attitude with the attitudes of political parties. Those applications need a mathematical metric to calculate the distance between two attitudes in an argumentation. But which properties should an intuitive metric fulfill? We surveyed untrained persons to find out which properties such a metric should have to produce results matching human intuition. For that, we formulated several hypotheses for useful properties, which we then tried to confirm in our survey. As a result, we were able to identify some properties a metric for comparing attitudes represented as argumentation graphs should have, and some properties where further research is needed.",
    keywords = "argumentation graphs, metric, human baseline",
    pages = "1--15",
    url = "http://ceur-ws.org/Vol-2777/paper21.pdf",
    year = "2020",
    month = "11",
    booktitle = "Proceedings of the Workshop on Advances In Argumentation In Artificial Intelligence 2020",
    number = "2777",
    series = "CEUR Workshop Proceedings",
    address = "Aachen",
    eventdate = "2020-11-25"
}