MOOD: Massive Open Online Deliberation Platform-A Practical Application

E.P. Verdiesen, Virginia Dignum, Jeroen van den Hoven, Martijn Cligge, Jan Timmermans, Lennard Segers

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)
58 Downloads (Pure)


Nowadays, public debates often take place on social media platforms like Facebook or Twitter and can be characterized as asynchronous, protracted and ill-structured. The Massive Open Online Deliberation (MOOD) platform aims to structure these debates. Essential is that the platform can differentiate between the moral acceptability and the social acceptance of a debate outcome. We briefly describe the e-deliberation process and look at two existing debate platforms, Liquidfeedback and Debatehub. We design and build a prototype that mainly focuses on:(1) a method to differentiate and validate facts and opinions, and (2) a mechanism that maps both the social acceptance and the moral acceptability of debate outcomes. We research these ethical concepts more in depth and implement several techniques, such as a voting mechanism, in a working prototype that supports a four stage deliberation process. In future applications, machine learning techniques can be integrated in the platform to perform sentiment analysis on a debate.
Original languageEnglish
Title of host publicationProcedings of 22nd European Conference on Artificial Intelligence
Editors Gal A. Kaminka, Maria Fox, Paolo Bouquet, Eyke Hüllermeier, Virginia Dignum, Frank Dignum, Frank van Harmelen
PublisherIOS Press
ISBN (Electronic)978-1-61499-672-9
Publication statusPublished - 2016


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