1 Using Field Experimentation to Understand Information Quality in User-generated Content Roman Lukyanenko Florida International University roman@nlnature.com Jeffrey Parsons Memorial University of Newfoundland jeffreyp@mun.ca Introduction The rise and increased ubiquity of online interactive technologies such as social media or crowdsourcing (Barbier et al. 2012; de Boer et al. 2012; Doan et al. 2011; Whitla 2009) creates a fertile environment for field experimentation, affording researchers the opportunity to develop, test and deploy innovative design solutions in a live setting. In this research, we use a real crowdsourcing project as an experimental setting to evaluate innovative approaches to conceptual modeling and improve quality of user-generated content (UGC). Organizations are increasingly looking to harness UGC to better understand customers, develop new products, and improve quality of services (e.g., healthcare or municipal) (Barwise and Meehan 2010; Culnan et al. 2010; Whitla 2009). Scientists and monitoring agencies sponsor online UGC systems - citizen science information systems - that allow ordinary users to provide observations of local wildlife, report on weather conditions, track earthquakes and wildfires, or map their neighborhoods (Flanagin and Metzger 2008; Haklay 2010; Hand 2010; Lukyanenko et al. 2011). Despite the growing reliance on UGC, a pervasive concern is the quality of data produced by ordinary people. Online users are typically volunteers, resulting in a user base with diverse motivations and variable domain knowledge (Arazy et al. 2011; Coleman et al. 2009). When dealing with casual contributors external to the organization, traditional approaches to information quality (IQ) management break down (Lukyanenko and Parsons 2011; Parsons and Lukyanenko 2011). Traditionally, information production processes are assumed to be designed to support the needs of data consumers – typically employees or others associated with the sponsoring organizations that require information for decision-making and other tasks (Lee and Strong 2003; Redman 1996). Consequently, data production typically conforms to the way the data is to be used. For example, as biological species is a major unit of scientific analysis, the prevailing practice in online citizen science (e.g., see www.eBird.org ) is to require online volunteers to classify the observed phenomena (e.g., birds) at the species-level of specificity