Trust is a fundamental prerequisite in the growth and sustainability of sharing economy platforms. Many of such platforms rely on actions that require trust to take place, such as entering a stranger’s car or sleeping at a stranger’s place. For this reason, understanding, measuring, and tracking trust can be of great benefit to such platforms, enabling them to identify trust behaviors, both online and offline, and identify groups which may benefit from trust-building interventions. In this work, we present the design and evaluation of a behavioral framework to measure a user’s propensity to trust others on Airbnb. We conducted an online experiment with 4,499 Airbnb users in the form of an investment game in order to capture users’ propensity to trust other users on Airbnb. Then, we used the experimental data to generate both explanatory and predictive models of trust propensity. Our contribution is a framework that can be used to measure trust propensity in sharing economy platforms via online and offline signals. We discuss which affordances need to be in place so that sharing economy platforms can get signals of trust, in addition to how such a framework can be used to inform design around trust in the short and long term.

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.

In this study we extend research on online collaboration by examining motivation to do work on the crowdsoucing service Amazon Mechanical Turk (MTurk). We address a challenge to many existing studies of motivation in online contexts: they are based on survey self-reports, which are susceptible to effects such as social desirability bias. In addition we investigate a second challenge to the extant research on motivation in the context of MTurk: a failure to examine potential differences between MTurk workers (Turkers) from different parts of the world, especially those from the US and India, MTurk's two largest worker groups. Using a survey technique called the list experiment, we observe distinct profiles of motivation and patterns of social desirability effects among Turkers in the US and India. Among US Turkers, we find that social desirability encourages over-reporting of each of four motivating factors we examined. The over-reporting was particularly large in the case of money as a motivator. In contrast, among Turkers in India we find a more complex pattern of social desirability effects, with workers under-reporting "killing time" and "fun" as motivations, and drastically over-reporting "sense of purpose." We conclude by discussing these results and proposing implications for future research and design.

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.

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.

Abstract 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.

Perceptions of information products such as Wikipedia can depend on assumptions and stereotypes about the people who create them. As new Wikipedians consider contributing they are likely to apply such assumptions and ask themselves: "Are Wikipedia contributors my kind of people? Is this a group I'd like to belong to?" In this qualitative study I address the potential challenge of these questions by exploring readers and infrequent editors' perceptions of Wikipedia contributors and their motivations. Through analysis of twenty semi-structured interviews, I find evidence of strong negative perceptions as well as positive ones which nonetheless prevent users from identifying with active Wikipedia contributors. I argue that these perceptions present a barrier to the progression of participation over time. I conclude by discussing the practical challenges of my findings for Wikipedia and other online collaborative systems.

The success of Wikipedia as a large-scale collaborative effort has spurred researchers to examine the motivations and behaviors of Wikipedia's participants. However, this research has tended to focus on active involvement rather than more common forms of participation such as reading. In this paper we argue that Wikipedia's readers should not all be characterized as free-riders -- individuals who knowingly choose to take advantage of others' effort. Furthermore, we illustrate how readers provide a valuable service to Wikipedia. Finally, we use the notion of legitimate peripheral participation to argue that reading is a gateway activity through which newcomers learn about Wikipedia. We find support for our arguments in the results of a survey of Wikipedia usage and knowledge. Implications for future research and design are discussed.

This paper focuses on online gift exchange in a setting where online and offline interactions are tightly intertwined and most of the exchanges require face-to-face interaction to be completed. We present a local online gift exchange system, Kassi, and a seven-month case study of its use. Based on survey material and logs of system usage, we analyze users' motivations to contribute to the system and the community. While most users held favorable attitudes towards the system, many reasons for not using the service were found. We set our findings into perspective by discussing different ways of defining participation, measuring use, and qualifying different types of contributions. We argue that when users try to fit a system into their everyday lives, designers should consider supporting such efforts - even if user behavior does not match expectations. Designers who encourage emergent and unanticipated behaviors can enhance users' sense of participation and encourage the leap from intention to realized action.

Abstract In this article, we explore the dynamics of prosocial and self‐interested behavior among musicians on MySpace Music. MySpace Music is an important platform for social interactions and at the same time provides musicians with the opportunity for significant profit. We argue that these forces can be in tension with each other, encouraging musicians to make strategic choices about using MySpace to promote their own or others' rewards. We look for evidence of self‐interested and prosocial “friending” strategies in the social network created by Top Friends links. We find strong evidence that individual preferences for prosocial and self‐interested behavior influence friending strategies. Furthermore, our data illustrate a robust relationship between increased prominence and increased attention to others' rewards. These results shed light on how musicians manage their interactions in complex online environments and extend research on social values by demonstrating consistent preferences for prosocial or self‐interested behavior in a multifaceted online setting.

Abstract In this article, the authors develop hypotheses about three key correlates of attitudes about discretionary online behaviors and control over one's own online information: frequency of engaging in risky online behaviors, experience of an online adverse event, and the disposition to be more or less trusting and cautious of others. Through an analysis of survey results, they find that online adverse events do not necessarily relate to greater overall Web discretion, but they do significantly associate with users' perceptions of Web information control. However, the frequencies with which individuals engage in risky online activities and behaviors significantly associate with both online discretion and information control. In addition, general dispositions to trust and be cautious are strongly related to prudent Internet behavior and attitudes about managing personal online information. The results of this study have clear consequences for our understanding of behaviors and attitudes that might lead to greater online social intelligence, or the ability to make prudent decisions in the presence of Internet uncertainties and risks. Implications for theory and practice are discussed.

This paper describes research focused on understanding the relationships between operational knowledge about how online collective action systems work and participation in those systems. Specifically, I use qualitative interviews to examine knowledge about online systems that form public goods, questioning whether the notions of public goods and social dilemmas are relevant and meaningful for individuals making real-world participation decisions. This paper also describes concurrent experimental research focused on exploring the relationship between knowledge about one's own and others' competence to contribute to collective goals as a factor in participation decisions.

Kiosk/telecenter projects, which provide access to information technologies and related services, have become an important vehicle for delivering the benefits of ICTs to the developing world. As more of these projects are implemented, there is a real need to evaluate their long-term impacts. Assessments, however, are often restricted to economic and technical factors which, though important, by themselves privilege objects and models over people and situated use. Beginning with the premise that successful and sustainable kiosk projects are those that adapt to local contexts and cultures, this paper presents a holistic model for assessment that centers on cultural factors. We argue that understanding cultural factors allows kiosk projects to respond to local histories, needs, and values in a way that ultimately enhances the long-term viability of change. After discussing some challenges that many kiosks currently face with respect to a holistic, culture-centered assessment, we present a brief review of models for 'rapid' ethnography which are drawn from applied anthropology. Finally, we propose a focused and practical framework for holistic analysis to inform program development