Gamification, the use of game mechanics in non-gaming applications, has been applied to various systems to encourage desired user behaviors. In this paper, we examine patterns of user activity in an enterprise social network service after the removal of a points-based incentive system. Our results reveal that the removal of the incentive scheme did reduce overall participation via contribution within the SNS. We also describe the strategies by point leaders and observe that users geographically distant from headquarters tended to comment on profiles outside of their home country. Finally, we describe the implications of the removal of extrinsic rewards, such as points and badges, on social software systems, particularly those deployed within an enterprise.

US federal legislation is a common subject of discussion and advocacy on the web, inspired by the open government movement. While the contents of these bills are freely available for download, understanding them is a significant challenge to experts and average citizens alike due to their length, complex language, and obscure topics. To make these important documents more accessible to the general public, we present Many Bills (http://manybills.us): a web-based set of visualization tools that reveals the underlying semantics of a bill. Using machine learning techniques, we classify each bill's sections based on existing document-level categories. We then visualize the resulting topic substructure of these bills. These visualizations provide an overview-and-detail view of bills, enabling users to read individual sections of a bill and compare topic patterns across multiple bills. Through an overview of the site's user activity and interviews with active users, this paper highlights how Many Bills makes the tasks of reading bills, identifying outlier sections in bills, and understanding congressperson's legislative activity more manageable.

In this work we analyze the behavior on a company-internal social network site to determine which interaction patterns signal closeness between colleagues. Regression analysis suggests that employee behavior on social network sites (SNSs) reveals information about both professional and personal closeness. While some factors are predictive of general closeness (e.g. content recommendations), other factors signal that employees feel personal closeness towards their colleagues, but not professional closeness (e.g. mutual profile commenting). This analysis contributes to our understanding of how SNS behavior reflects relationship multiplexity: the multiple facets of our relationships with SNS connections.

As online communities, such as social network sites, mature, they face challenges in sustaining user engagement. To address this, we designed and deployed a rating system to encourage a broad set of users to promote a diverse set of content on a social network site. By evaluating the impact of the promotions on the site's content and users over time, we found that the system successfully promotes more diverse content and encourages new social interactions between users.

The rise in the use of social network sites allows us to collect large amounts of user reported data on social structures and analysis of this data could provide useful insights for many of the social sciences. This analysis is typically the domain of Social Network Analysis, and visualization of these structures often proves invaluable in understanding them. However, currently available visual analysis tools are not very well suited to handle the massive scale of this network data, and often resolve to displaying small ego networks or heavily abstracted networks. In this paper, we present Honeycomb, a visualization tool that is able to deal with much larger scale data (with millions of connections), which we illustrate by using a large scale corporate social networking site as an example. Additionally, we introduce a new probability based network metric to guide users to potentially interesting or anomalous patterns and discuss lessons learned during design and implementation. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Success and sustainability of social networking sites is highly dependent on user participation. To encourage contribution to an opt-in social networking site designed for employees, we have designed and implemented a feature that rewards contribution with points. In our evaluation of the impact of the system, we found that employees are initially motivated to add more content to the site. This paper presents the analysis and design of the point system, the results of our experiment, and our insights regarding future directions derived from our post-experiment user interviews.

Social networking sites support a variety of shared content types such as photos, videos, or music. More structured or form-based social content types are not mainstream but we have started seeing sites evolve that support them. This paper describes the design and use of structured lists in an enterprise social networking system. As a major feature of our shared lists, we introduced the ability to reuse someone else's list. We report the results on the use and reuse of shared lists based on three months of usage data from 285 users and interviews with 9 users. Our findings suggest that despite the structured nature of lists, our users socialize more around lists than photos, and use lists as a medium for self-representation.

The introduction of a social networking site inside of a large enterprise enables a new method of communication between colleagues, encouraging both personal and professional sharing inside the protected walls of a company intranet. Our analysis of user behavior and interviews presents the case that professionals use internal social networking to build stronger bonds with their weak ties and to reach out to employees they do not know. Their motivations in doing this include connecting on a personal level with coworkers, advancing their career with the company, and campaigning for their projects.

User profiles on today's social networking sites support only a small set of predefined questions. We report on an alternative way for users to richly describe themselves, by entering not only responses, but their own questions as well. Data from 10 months of usage shows that users of a social networking site created thousands of diverse questions and reused existing questions from other users. Our findings suggest that those with highly diverse user profiles have a higher number of friends.

As the use of social networking websites becomes increasingly common, the types of social relationships managed on these sites are becoming more numerous and diverse. This research seeks to gain an understanding of the issues related to managing different social networks through one system, in particular looking at how users of these systems present themselves when they are using one site to keep in contact with both their past social groups from school and their current social connections in the workplace. To do this, we examined online profile pages and interviewed employees at a large software development company who frequently use the website Facebook, a site primarily used by college students and young graduates transitioning into the work force. The outcome of this initial case study is a framework for understanding how users manage self-presentation while maintaining social relationships in heterogeneous networks.

This article presents Second Messenger, a system of dynamic awareness displays that reveal speaker participation patterns in a face-to-face discussion. The system has been used by a variety of groups during face-to-face meetings, increasing individuals ’ awareness of their own and others ’ participation in discussions. Experimental results indicate that these displays influence the amount an individual participates in a discussion and the process of information sharing used during a decision-making task. These findings suggest that awareness applications bring about systematic changes in group communication styles, highlighting the potential for such applications to be designed to improve group interactions. Joan DiMicco is an HCI researcher interested in human–human communication; she is a researcher at IBM Research in Cambridge, MA. Katherine Hollenbach is an undergraduate at MIT with an interest in computer science and design; she is an undergraduate researcher at the MIT Media Lab. Anna

In this paper, we describe a range of audio problems that impact the effectiveness of audio conferences and detail the solutions we have devised to address these problems. We conducted an audio quality assessment to determine how differences in quality impact audio clarity, a remote person's experience connecting to a conference room , and social presence. Based on the results of this assessment, we examine the costs and benefits of increasing audio fidelity with respect to the network resources needed to support high-fidelity audio conferencing.

During face-to-face interactions, groups frequently overly rely on the dominant viewpoint to lead the group in its decision-making process. We begin with a discussion of this phenomenon and the possibility for technology to assist in addressing it. We then present findings from a behavioral study that examines how a shared display of individual speaker-participation rates can impact the behavior of the group during a collaboration task. The results from the study indicate that the presence of such a display influences the behavior of group participants in the extremes of over and under participation. While influencing the quantity of time someone speaks is not directly equivalent to influencing the topics discussed, we suggest that this approach of providing peripheral displays of social information is promising for improving certain types of group interactions.

This paper introduces the application Second Messenger, a tool for supporting face-to-face meetings and discussions. Second Messenger uses a speech-recognition engine as an input method and outputs filtered keywords from the group's conversation onto an interactive display. The goal of this interface is to improve the quality of a group discussion by increasing the visibility of diverse viewpoints.

In the near future, dynamic pricing will be a common competitive maneuver. In this age of digital markets, sellers in electronic marketplaces can implement automated and frequent adjustments to prices and can easily imagine how this will increase their revenue by selling to buyers "at the right time, at the right price." But at present, most sellers do not have an adequate understanding of the performance of dynamic pricing algorithms in their marketplaces. This paper addresses this concern by analyzing the performance of two adaptive pricing algorithms. We study the behavior of these algorithms within the Learning Curve Simulator, a platform for analyzing dynamic pricing strategies in finite markets assuming various buyer behaviors. The goals of our research are twofold: (i) to explore the use of simulation as a tool to aid in the development of dynamic pricing strategies; and (ii) to explicitly identify the market conditions under which our example strategies, Goal-Directed and Derivative-Following, are successful.

This paper examines seller strategies for dynamic pricing in an auction-driven marketplace.Specifically, this paper focuses on the airline industry, a field experienced in demand forecasting and dynamic pricing capabilities.Our goal is to discover the relevant factors when a seller dynamically evaluates incoming bids on a finite number of goods.We present two adaptive pricing strategies and evaluate them using a market simulator.