As online communities grow and the volume of usergenerated content increases, the need for community management also rises. Community management has three main purposes: to create a positive experience for existing participants, to promote appropriate, socionormative behaviors, and to encourage potential participants to make contributions. Research indicates that the quality of content a potential participant sees on a site is highly influential; off-topic, negative comments with malicious intent are a particularly strong boundary to participation or set the tone for encouraging similar contributions. A problem for community managers, therefore, is the detection and elimination of such undesirable content. As a community grows, this undertaking becomes more daunting. Can an automated system aid community managers in this task? In this paper, we address this question through a machine learning approach to automatic detection of inappropriate negative user contributions. Our training corpus is a set of comments from a news commenting site that we tasked Amazon Mechanical Turk workers with labeling. Each comment is labeled for the presence of profanity, insults, and the object of the insults. Support vector machines trained on these data are combined with relevance and valence analysis systems in a multistep approach to the detection of inappropriate negative user contributions. The system shows great potential for semiautomated community management.

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A growing number of systems on the Internet create what we call information pools, or collections of online information goods for public, club or private consumption. Examples of information pools include collaborative editing websites (e.g. Wikipedia), peer-to-peer file sharing networks (e.g., Napster), multimedia contribution sites (e.g. YouTube), and amorphous collections of commentary (e.g., blogs). In this study, we specifically focus on information pools that create a public good. Following current theory and research, we argue that extremely low costs of contribution combined with very large networks of distribution facilitate the production of online information poolsdespite an abundance of free-riding behavior. This paper presents results from a series of Internet field experiments that examine the effects of various feedback mechanisms on repeat contributions to an information pool. We demonstrate that the social psychological benefits from gratitude, historical reminders of past behavior, and ranking of one's contributions relative to those of others can significantly increase repeat contributions. In addition, the context in which individuals interact with the system may partially mitigate the positive effect of some types of feedback on contribution behavior. doi:10.1111/j. 1083-6101.2008.00416.x Introduction Systems that facilitate computer-mediated exchanges of digital information grew along with the Internet in the early 1990's, providing new ways to quickly and efficiently share text, music, movies, software and other digital goods. The purpose of these systems varies widely, from the production of an 'online encyclopedia that anyone can edit' (Wikipedia) to the distribution of digital media (e.g. peer-to-peer systems such as the original Napster). Though diverse in purpose, each system provides different types of incentives that can encourage individuals to contribute information. A growing body of research looks at how non-monetary incentives harness social psychological processes to promote increased contributions (e.g., Ling et al., 2005;Rashid et al., 2006;Cheshire, 2007). This paper complements this line of research by focusing on the question of how non-monetary incentives encourage individuals to contribute small quantities of information in different contexts of interaction. We report the results of a field experiment which examines the effects of three different synchronous, direct feedback mechanisms on repeat contributions to an online system of information exchange.When digital information goods from many different sources are collectively transmitted over a computer network so that they can be accessed by groups of individuals, they create an information pool. In these systems, individual contributions of digital information combine to produce information products for public, club, or private consumption.1 Digital information goods may include (but are not limited to) software, photographs, art, music, speeches/lectures, videos, and general discourse (Kollock, 1999;Shapiro and V...