Misalignment Between Supply and Demand of Quality Content in Peer Production Communities

Abstract

In peer production communities, individual community members typically decide for themselves where to make contributions, often driven by factors such as “fun” or a belief that “information should be free”. However, the extent to which this bottom-up, interest-driven content production paradigm meets the needs of consumers of this content is unclear. In this paper, we introduce an analytical framework for studying the relationship between content production and consumption in peer production communities. Applying our framework to four large Wikipedia language editions, we find extensive misalignment between production and consumption in all of them. We also show that this misalignment has an enormous effect on Wikipedias readers. For example, over 1.5 billion monthly pageviews in the English Wikipedia go to articles that would be of much higher quality if editors optimally distributed their work to meet reader demand. Examining misalignment in more detail, we observe that there is an excess of high-quality content about certain specific topics, and that the majority of articles with insufficient quality are in a stable state (i.e. not breaking news). Finally, we discuss technolo- gies and community practises that can help reduce the misalignment between the supply of and demand for high-quality content in peer production communities.

A Large-Scale Characterization of How Readers Browse Wikipedia

“…Complementary metrics capturing the usage of articles, such as the shape, describe important properties that might act as confounding factors, and other studies should consider them in stratified analyses. In addition, they could also be directly useful for editors providing additional information about the audience of articles, potentially helping to bridge the gap reported in the misalignment between supply and demand of articles (Warncke-Wang et al 2015).…”

Quantifying the Impact of Biopics on Wikipedia Articles

“…The quality and completeness of Wikipedia articles have attracted the attention of researchers from various domains to study online collaboration dynamics (Kittur & Kraut, Beyond Wikipedia: coordination and conflict in online production groups, 2010) (Johnson, et al, 2016) (Kittur & Kraut, Harnessing the wisdom of crowds in wikipedia: quality through coordination, 2008) (Ren & Yan, 2017) to examine its impact on other online collaborative portals (Vincent, Johnson, & Hecht, 2018) (Warncke-Wang, Ranjan, Terveen, & Hecht, 2015, and to train state-of-the-art artificial intelligence algorithms (Hoffart, Suchanek, Berberich, & Weikum, 2013) (Medelyan, Milne, Legg, & Witten, 2009) (Mikolov, Sutskever, Chen, Corrado, & Dean, 2013). Even with such a vast domain coverage, researchers have mainly focused on analyzing Wikipedia's content quality.…”