entries:
    Brenneis2020c:
        type: InProceedings
        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'
        author:
        -   first: Markus
            last: Brenneis
        -   first: Martin
            last: Mauve
        editor:
        -   first: Bettina
            last: Fazzinga
        -   first: Filippo
            last: Furfaro
        -   first: Francesco
            last: Parisi