Public deliberations are organised by governments and other large institutions to take the views of citizens around controversial issues. Increasing public demand and the associated burden on public funding make the quality of public deliberation events and their outcomes critical to modern democracies. This paper focuses on technology developed around streams of computational argument data intended to inform and improve deliberative communication in real time. Combining state-of-the-art speech recognition, argument mining, and analytics, we produce dynamic, interactive visualisations intended for non-experts, deployed incrementally in real time to deliberation participants via large screens, hand-held and personal computing devices. The goal is to bridge the gap between theoretical criteria on deliberation quality from the political sciences and objective analytics calculated automatically from computable argument data in actual public deliberations, presented as a set of visualisations which work on stream data and are simple, yet informative enough to make a positive impact on deliberative outcomes.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:32801 |
| Date | 25 January 2019 |
| Creators | Plüss, Brian, Sperrle, Fabian, Gold, Valentin, El-Assady, Mennatallah, Hautli-Janisz, Annette, Budzynska, Katarzyna, Reed, Chris |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | urn:nbn:de:bsz:15-qucosa2-327974, qucosa:32797 |
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