Joan Morris DiMicco’s research while affiliated with IBM and other places

Embodiments of the present invention address deficiencies of the art in respect to social media navigation in a social network and provide a method, system and computer program product for user pivot for media sharing in a social network. In an embodiment of the invention, a social networking data processing system for user pivot navigation. The system can include a social network executing in a host server, the social network providing shared media for viewing by communicatively coupled clients. The system also can include user pivot logic coupled to the social network. The logic can include program code enabled to identify a user of the social network associated with shared media selected for display in a media view to a requesting user and to generate a navigation view in a consistent location in a user interface to the social network irrespective of a page view rendered in the user interface, the view providing a link to a profile page for the identified user.

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.

US Federal Legislation is a common subject of discussion and advocacy on the web. The contents of bills present a significant challenge to both experts and average citizens due to their length and complex legal language, To make bills more accessible to the general public, we present Many Bills: a web-based visualization prototype that reveals the underlying semantics of a bill, We classify the sections of a bill into topics and visualize them using different colors, Further, using information retrieval techniques, we locate sections that don't seem to fit with the overall topic of the bill. To highlight outliers in our 'misfit mode', we visualize them in red, which builds a contrast against the remaining gray sections, Both topic and misfit 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, We obtained initial user feedback and continue collecting label corrections from users through the interface. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

Social networking sites have been deployed within global enterprises to encourage informal communication and build social capital between its globally distributed members. Such interactions can potentially contribute to intercultural learning opportunities; however, it is unclear whether cross-geographical social contact consistently occurs. We present initial results from a quantitative analysis of user activity from a global enterprise SNS, observe asymmetries in directionality and reciprocity in connections in various geographic regions as well as a strong tendency towards geographic homophily. Finally, we suggest that language proficiency may play a role in differences in regional adoption of the system.

Users of online communities are commonly classified into active members versus lurkers. In this work we experiment with a method designed to encourage lurkers to share their acquired social capital with a community through lightweight contribution. The findings from our experiment demonstrate the importance of exploring new ways lurker populations can offer additional benefits to online communities.

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.

Users of online communities are commonly classified into active members versus lurkers. In this work we experiment with a method designed to encourage lurkers to share their acquired social capital with a community through lightweight contribution. The findings from our experiment demonstrate the importance of exploring new ways lurker populations can offer additional benefits to online communities. Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

A new generation of Web tools is penetrating into organizations after their successful adoption within the consumer domain (e.g., social networking; sharing of photos, videos, tags, or bookmarks; wiki-based editing). These tools and the collaborative processes that they support on the large scale are often referred to as Collective Intelligence (CI). The workshop discusses CI tools for collaboration in work-related settings, especially for task forces now increasingly common in industry or government. The aims are refining the problem, summarizing pioneer work on CI, and ultimately develop a research agenda on the problem of supporting CI among workers in organizations. CI studies, tools, and new research methods for studying CI are discussed.

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 of- ten 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 il- lustrate 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 dur- ing design and implementation.