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.
|Title of host publication||Procedings of 22nd European Conference on Artificial Intelligence|
|Editors||Gal A. Kaminka, Maria Fox, Paolo Bouquet, Eyke Hüllermeier, Virginia Dignum, Frank Dignum, Frank van Harmelen|
|Publication status||Published - 2016|
Verdiesen, E. P., Dignum, V., van den Hoven, J., Cligge, M., Timmermans, J., & Segers, L. (2016). MOOD: Massive Open Online Deliberation Platform-A Practical Application. In G. A. Kaminka, M. Fox, P. Bouquet, E. Hüllermeier, V. Dignum, F. Dignum, & F. van Harmelen (Eds.), Procedings of 22nd European Conference on Artificial Intelligence IOS Press.