This paper is focused on the computational analysis of collective discourse, a collective behavior seen in nonexpert content contributions in online social media. We collect and analyze a wide range of real-world collective discourse datasets from movie user reviews to microblogs and news headlines to scientic citations. We show that all these datasets exhibit diversity of perspective, a property seen in other collective systems and a criterion in wise crowds. Our experiments also conrm that the network of dierent perspective co-occurrences exhibits the small-world property with high clustering of dierent perspectives. Finally, we show that non-expert contributions in collective discourse can be used to answer simple questions that are otherwise hard to answer.
['Vahed Qazvinian', 'Dragomir R. Radev']
A Computational Analysis of Collective Discourse