Understanding collaborative knowledge-building dynamics

Abstract

The way we used to gather knowledge about things some two decades back was very different. The knowledge sources available to us, such as books or other offline materials, were written by experts, mostly in a centralized manner. Also, it was not-so-easy to find answers to our questions. With the advancements in Internet technology, currently, we have several online portals facilitating the process of building knowledge. The examples include Wikipedia, StackOverflow, Github, Quora etc. However, out of all the portals that come up, only a few become successful in tapping the crowd’s potential. For instance, just likeWikipedia, many other encyclopedias such as Interpedia, Nupedia, Citizendium came up. However, they had to be shut down very soon. Similarly, in Q&A portals, Google Answers came up, which had to be closed down within four years. Yahoo! Answers that was once very successful is not much used these days due to many reasons, including its inefficient maintenance policies. These instances indicate the prevalence of hit-and-trial ways of creating knowledge-building portals. All collaborative systems aim to achieve what is called collective intelligence that emerges when a group of individuals collaborates. However, from a group of people collaborating, collective stupidity is also equally likely to occur. Research shows that four criteria need to be satisfied, viz., diversity of opinion, independence, decentralization, and aggregation for a crowd to be effective. However, these criteria mostly suited offline settings. In online collaborative setups, many other parameters need to be considered. As an instance, due to the openness in online setups, people get affected by others’ contributions. Hence, their contributions may not be completely independent. Also, due to the large-scale collaboration, other parameters such as inequality of contribution by users, role-playing behavior of users, and the conflicts arising among the contributors also affect collaborative output in online portals. Since they affect the knowledge-building capacity of such portals, it is important to examine these parameters to avoid their failure to achieve the intended objective. The work done in the knowledge-building domain so far is mostly theoretical or based on small-scale control groups. Since it is possible to gather the underlying data of collaboration from online portals in the current times, the focus of this thesis is to analyze this data to fill the prevalent gaps in our understanding of the dynamics of collaborative knowledge-building. The insights thus obtained may help in building improved portals and effective policies.