Professor in Political Science and Computer and Information Science

David Lazer

Wiki-worthy: collective judgment of candidate notability

Publication date: 
08/2015
Authors: 
Drew B. Margolin
Brian Keegan
Sasha Goodman
Yu-Ru Lin
David Lazer
Wiki-worthy: collective judgment of candidate notability

The use of socio-technical data to predict elections is a growing research area. We argue that election prediction research suffers from under-specified theoretical models that do not properly distinguish between 'poll-like' and 'prediction market-like' mechanisms understand findings. More specifically, we agrue that, in systems with strong norms and reputational feedback mechanisms, individuals have market-like incentives to bias content creation toward candidates they expect will win. We provide evidence for the merits of this approach using the creation of Wikipedia pages for candidates in the 2010 US and UK national legislative elections. We find that Wikipedia editors are more likely to create Wikipedia pages for challengers who have a better chance of defeating their encumbent opponent and that the timing of these page creations coincides with periods when collective expectations for candidate's success are relatively high.

Research Areas TOC

Computational Social Science, Collective Cognition

Computational Social Science, 21st Century Democracy, Political Networks

Computational Social Science, Political Networks

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