Abstract
We present an approach for the prediction of user authorship and feedback behavior with shared content. We consider that users use models of other users and their feedback to choose what to publish next. We look at the problem as a game between authors and audiences and relate it to current content-based user modeling solutions with no prior strategic models. As applications, we consider the large-scale authorship of Wikipedia pages, movies and food recipes. We demonstrate analytic properties, authorship and feedback prediction results, and an overall framework to study content authorship regularities in social media.
Highlights
We study new tools to model user authoring behavior in online media
Many regularities have been found in the online feedback behavior of users (Goyal et al 2010; Radinsky et al 2012; Szabó and Huberman 2010; Lerman 2007; Das and Lavoie 2014), but much fewer patterns have been discovered in online content creation
If we look at Wikipedia this way, its byproduct is a medium that increases in precision with an increasing user base
Summary
We study new tools to model user authoring behavior in online media. By remaining agnostic to the surrounding media (its author base constitution, incentives, audience, practices, etc.), standard topic and content models (Pennacchiotti and Gurumurthy 2011; Hu et al 2015; Hong and Davison 2010; Cha and Cho 2012) might miss patterns relevant to understanding and predicting user behavior. The problem in this case is to predict which and how many pages individual users are likely to edit in the future. Despite the formal model, the main question addressed is practical: whether is possible to exploit regularities such as the previous (formally described by a game) to predict user behavior
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