The rise of social media in the enterprise has enabled new ways for employees to speak up and communicate openly with colleagues. This rich textual data can potentially be mined to better understand the opinions and sentiment of employees for the benefit of the organization. In this paper, we introduce Enterprise Social Pulse (ESP) -a tool designed to support analysts whose job involves understanding employee chatter. ESP aggregates and analyzes data from internal and external social media sources while respecting employee privacy. It surfaces the data through a user interface that supports organic results and keyword search, data segmentation and filtering, and several analytics and visualization features. An evaluation of ESP was conducted with 19 Human Resources professionals. Results from a survey and interviews with participants revealed the value and willingness to use ESP, but also surfaced challenges around deploying an employee social media listening solution in an organization.

Reading congressional legislation, also known as bills, is often tedious because bills tend to be long and written in complex language. In IBM Many Bills, an interactive web-based visualization of legislation, users of different backgrounds can browse bills and quickly explore parts that are of interest to them. One task users have is to be able to locate sections that don't seem to fit with the overall topic of the bill. In this paper, we present novel techniques to determine which sections within a bill are likely to be outliers by employing approaches from information retrieval. The most promising techniques first detect the most topically relevant parts of a bill by ranking its sections, followed by a comparison between these topically relevant parts and the remaining sections in the bill. To compare sections we use various dissimilarity metrics based on Kullback-Leibler Divergence. The results indicate that these techniques are more successful than a classification based approach. Finally, we analyze how the dissimilarity metrics succeed in discriminating between sections that are strong outliers versus those that are 'milder' outliers.

Abstract

We introduce a programming environment entitled Share that is designed to encourage loosely bound cooperation between individuals within communities of practice through the sharing of code. Loosely bound cooperation refers to the opportunity community members have to assist and share resources with one another while maintaining their autonomy and independent practice. We contrast this model with forms of collaboration that enable large numbers of distributed individuals to collaborate on large scale works where they are guided by a shared vision of what they are collectively trying to achieve. We hypothesize that providing fine-grained, publicly visible attribution of code sharing activity within a community can provide socially motivated encouragement for code sharing. We present an overview of the design of our tool and the objectives that guided its design and a discussion of a small-scale deployment of our prototype among members of a particular community of practice.

In this paper we describe Taking Sides, a performance using a real-time speech visualization software system called TextEngine. Taking Sides is a collaboration between our research studio and Montreal hip-hop artist Dwayne Hanley. Our primary goal was to create a strong conceptual link between the text visualization, the content of the artist's lyrics, and his performance style. Additionally we wanted to test the flexibility of TextEngine in developing customized performance applications. Pursuing these goals led us through a three month development effort that cycled tightly between design, performance and programmatic iterations.