Judd Antin

The Social Psychological Effects of Feedback on the Production of Internet Information Pools

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 pools—despite 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. Resume The Social Psychological Effects of Feedback on the Production of Internet Information Pools 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 pools—despite 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. Resumen Los Efectos Psicologicos y Sociales de la Retroalimentacion sobre la Produccion de Grupos de Informacion en el Internet Un numero creciente de sistemas en el Internet crean lo que nosotros llamamos Grupos de informacion, o colecciones de bienes de informacion online para el consumo publico, de club o privados. Ejemplos de grupos de informacion incluyen la colaboracion en la edicion de paginas del Internet (por ejemplo, Wikipedia), las redes de compartimiento de documentos entre los pares (por ejemplo, Napster), las contribuciones de sitios multimedia (por ejemplo, YouTube), y las colecciones amorfas de comentarios (por ejemplo, blogs). En este estudio, nos focalizamos especificamente en los grupos de informacion que crean bienes publicos. Siguiendo las teorias e investigaciones corrientes, proponemos que las contribuciones de extremado bajo costo combinado con las grandes redes de distribucion facilitan la produccion de grupos de informacion online—a pesar de la abundancia de comportamiento gratuito. Este articulo presenta resultados de una serie de experimentos de campo en Internet para examinar los efectos de varios mecanismos de retroalimentacion sobre las contribuciones reiteradas a un grupo de informacion. Demostramos que los beneficios sociales y psicologicos de la gratitud, recordatorios historicos del comportamiento pasado, y el rango de las contribuciones de algunos en relacion a aquellos otros puede incrementar significativamente las contribuciones repetidas. Ademas, el contexto en el cual los individuos interactuan con el sistema puede mitigar parcialmente el efecto positivo de algunos tipos de retroalimentacion sobre el comportamiento contributivo. ZhaiYao Yo yak

Automatic identification of personal insults on social news sites

As online communities grow and the volume of user-generated 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.

Profanity use in online communities

As user-generated Web content increases, the amount of inappropriate and/or objectionable content also grows. Several scholarly communities are addressing how to detect and manage such content: research in computer vision focuses on detection of inappropriate images, natural language processing technology has advanced to recognize insults. However, profanity detection systems remain flawed. Current list-based profanity detection systems have two limitations. First, they are easy to circumvent and easily become stale - that is, they cannot adapt to misspellings, abbreviations, and the fast pace of profane slang evolution. Secondly, they offer a one-size fits all solution; they typically do not accommodate domain, community and context specific needs. However, social settings have their own normative behaviors - what is deemed acceptable in one community may not be in another. In this paper, through analysis of comments from a social news site, we provide evidence that current systems are performing poorly and evaluate the cases on which they fail. We then address community differences regarding creation/tolerance of profanity and suggest a shift to more contextually nuanced profanity detection systems.

Technology-mediated contributions: editing behaviors among new wikipedians

The power-law distribution of participation characterizes a wide variety of technology-mediated social participation (TMSP) systems, and Wikipedia is no exception. A minority of active contributors does most of the work. While the existence of a core of highly active contributors is well documented, how those individuals came to be so active is less well understood. In this study we extend prior research on TMSP and Wikipedia by examining in detail the characteristics of the revisions that new contributors make. In particular we focus on new users who maintain a minimum level of sustained activity during their first six months. We use content analysis of individual revisions as well as other quantitative techniques to examine three research questions regarding the effect of early diversification of activity, nature vs. nurture, and associations with later administrative and organizational activity. We present analyses that address each of these questions, and conclude with implications for our understanding of the progression of participation on Wikipedia and other TMSP systems.

Mobile augmented reality: video prototyping

As mobile devices become more powerful, new features and user experiences become possible. A good example of such experiences is Augmented reality (AR). Achieved through the combination of current smart- phones processing capabilities and their embedded cameras, AR is a growing trend that offers an interesting approach for a wide variety of applications. However, coupling this new approach to the already demanding design process that characterizes mobile devices, further extends challenges to designers and developers. In this paper we present a preliminary study on prototyping and evaluation techniques for mobile AR. A short experiment within the context of an ongoing design project and initial results are presented along with some resulting guidelines.

Apples to oranges?: comparing across studies of open collaboration/peer production

This panel seeks to begin a discussion of how we can meaningfully compare and contrast between the diverse instances of open collaboration and peer production employed on the Internet today. Current research on the topic have tended to be too platform - (e.g. Wikipedia) or domain - (e.g. Open source) specific. The panelists will be tasked with addressing this problem using their own expertise and research projects to bear on the issue. Ultimately, the panel will seek to lay the foundations for the development of theoretical frameworks and principles for the design and application of open collaboration and CBPP based systems.

NONE OF US IS AS LAZY AS ALL OF US: Social intelligence and loafing in information pools

In this paper we apply theory and research from sociology and social psychology to the problem of collective information sharing and exchange on the internet. We investigate the relationships between pre-existing dispositions to be cautious towards others, the propensity to exert more or less effort as a function of group affiliation, and contribution towards a collective goal. We find that individuals with average or lower levels of general caution are more likely to contribute to a collective pool of information, providing support for Yamagishis (2001) argument that less cautious individuals exhibit a type of social intelligence by engaging in risky but potentially rewarding social interactions. Consistent with the literature on social loafing, we find that abstract group affiliations have a negative effect on information sharing behaviour. However, the effect of group affiliation is mediated by ones level of general caution. We argue that pre-dispositions to engage in socially risky situations are a critical element of individuals’ decisions to contribute to online information sharing systems or not.

Local experts and online review sites

Sites such as Yelp and Yahoo! Local provide a valuable source of knowledge about both new and familiar places. However, they represent an indirect source of local knowledge. Many are likely to prefer learning from the people who know their neighborhoods best: local experts. In this study of online review websites (ORWs), we examine attitudes about local knowledge and personal investment in local neighborhoods. We explore how these and other beliefs about local neighborhoods and local content may be related to interactions with ORWs. Finally, we argue that our findings suggest several important directions for future research and design investigations.

Algorithmic Anxiety and Coping Strategies of Airbnb Hosts

Algorithms increasingly mediate how work is evaluated in a wide variety of work settings. Drawing on our interviews with 15 Airbnb hosts, we explore the impact of algorithmic evaluation on users and their work practices in the context of Airbnb. Our analysis reveals that Airbnb hosts engage in a double negotiation on the platform: They must negotiate efforts not just to attract potential guests but also to appeal to only partially transparent evaluative algorithms. We found that a perceived lack of control and uncertainty over how algorithmic evaluation works can create anxiety among some Airbnb hosts. We present a framework for understanding this double negotiation, as well as a case study of coping strategies that hosts employ to deal with their anxiety. We conclude with a discussion of design solutions that can help reduce algorithmic anxiety and increase confidence in algorithmic systems.