A Computational Analysis of Collective Discourse
This work addresses the challenge of understanding collective behavior in online content for researchers in computational social science, but it is incremental as it applies known concepts to new data.
The paper tackled the problem of analyzing collective discourse in online social media by examining diverse datasets, finding that they exhibit diversity of perspective and small-world network properties, and demonstrating that non-expert contributions can answer simple questions effectively.
This paper is focused on the computational analysis of collective discourse, a collective behavior seen in non-expert 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 scientific 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 confirm that the network of different perspective co-occurrences exhibits the small-world property with high clustering of different perspectives. Finally, we show that non-expert contributions in collective discourse can be used to answer simple questions that are otherwise hard to answer.