Social media sites have become very popular within large enterprises. Still, employees are experiencing difficulties in engaging efficiently. In this paper, we present a study of a personalized action recommendation system in an enterprise social network. Following a previous study on how to raise one's social eminence in the enterprise and a set of interviews, we built an innovative recommendation system which provides employees with concrete personalized recommendations on how and where to engage. Differently from other systems, it presents recommendations in context of limiting social network behavioral patterns. The recommendations goal is to assist employees in growing out of these patterns. The paper presents the interview findings, the innovative recommendation system and results of a wide survey investigating the effectiveness of such a system.

Social media platforms such as blogs, wikis and file sharing have become very popular in enterprises. Despite their effectiveness in increasing collaboration in the organization, employees are overloaded with information originating from these many sources and find it hard to orient themselves in the stream of events occurring in their organizational news feed. In this paper we identify what makes an event in an organizational social media platform important to employees. Once important factors of an event to an employee are identified, the stream of events can be personalized and prioritized based on those and thus reduce the overload and assist in work efficiency. Through interviews and two extensive user surveys, the first hypothetical and the second empirical, we identified which factors of an event make it important and compare results from the hypothetical and empirical surveys.

In recent years, enterprise group chat collaboration tools, such as Slack, IBM»s Watson Workspace and Microsoft Teams, have presented unprecedented growth. With all the potential benefits of these tools - productivity increase and improved group communication - come significant challenges. Specifically, the 'always on' feature that makes it hard for users to cope with the load of conversational content and get up to speed after logging off for a while. In this demo, we present Collabot - a chat assistant service that implicitly learns users interests and social ties within a chat group and provides a personalized digest of missed content. Collabot assists users in coping with chat information overload by helping them understand the main topics discussed, collaborators, links and resources. This demo has two main contributions. First, we present a novel personalized group chat summarization algorithm; second the demonstration depicts a working implementation applied on different chat groups from different domains within IBM. A video, describing the demo can be found at https://www.youtube.com/watch?v=6cVsstiJ9vk.

Social media platforms have become popular in many enterprises. Employees build their social eminence by effectively engaging on these platforms. Becoming socially eminent in the organization is a personal journey and many employees need guidance to succeed. In this paper, we describe a tool called Personal Social Dashboard deployed within our enterprise. The tool provides feedback to employees on how effectively they engage in the enterprise social network by maintaining a set of scores covering different aspects of one's social role, such as Activity, Network, Reaction, and Eminence. We provide a description of the tool with a subsequent study of its use within the company and effect on employees' behavior in the company's social network.

Companies are motivating their employees to become socially engaged in enterprise social networks as a means to raise employee engagement. This is also beneficial for employees as it provides an opportunity for them to get a voice and raise their eminence. Unfortunately, not all employees are born "social butterflies" and many have difficulties in becoming more socially active. Failing to engage in an effective manner creates frustration which over time decreases their activity and lowers their chance to become socially eminent. This paper is a first of a kind study that reveals insights on social behavioral patterns of socially eminent employees. We extracted a comprehensive set of tips and recommendations to help employees become more socially eminent and investigate if and how eminent employees engage differently than others. We conducted interviews with top socially eminent employees and a quantitative inspection of bloggers behavioral patterns. Furthermore, we show that indeed best practices stated by socially eminent employees are fulfilled by eminent bloggers and less by others. We also found differences in how socially eminent employees engage compared to less eminent employees.

Although online communities have become popular both on the web and within enterprises, many of them often experience low levels of activity and engagement from their members. Previous studies identified the important role of community leaders in maintaining the health and vitality of their communities. One of their key means for doing so is by contributing relevant content to the community. In this paper, we study the effects of recommending social media content on enterprise community leaders. We conducted a large-scale user survey with four recommendation rounds, in which community leaders indicated their willingness to share social media items with their communities. They also had the option to instantly share these items. Recommendations were generated based on seven types of community interest profiles that were member-based, content-based, or hybrid. Our results attest that providing content recommendations to leaders can help uplift activity within their communities.

Online communities within the enterprise offer their leaders an easy and accessible way to attract, engage, and influence others. Our research studies the recommendation of social media content to leaders (owners) of online communities within the enterprise. We developed a system that suggests to owners new content from outside the community, which might interest the community members. As online communities are taking a central role in the pervasion of social media to the enterprise, sharing such recommendations can help owners create a more lively and engaging community. We compared seven different methods for generating recommendations, including content-based, member-based, and hybridization of the two. For member-based recommendations, we experimented with three groups: owners, active members, and regular members. Our evaluation is based on a survey in which 851 community owners rated a total of 8,218 recommended content items. We analyzed the quality of the different recommendation methods and examined the effect of different community characteristics, such as type and size.

Activity streams have become prevalent on the web and are starting to emerge in enterprises. In this work, we present Streamz, a novel application that uses a faceted search approach to provide employees with advanced capabilities of search, navigation, attention management, and other types of analytics on top of an enterprise activity stream. We provide a detailed description of the Streamz tool as well as usage analysis based on user interface logs and interviews of active users.

We propose to develop a framework which provides the ability to apply complex event processing in realtime domains, thus allowing an easier process of developing and maintaining specific solutions for real-time event-based systems, while upholding the real time requirements of the system. Specifically, we propose to develop a framework that includes an integrated development environment for defining rules, and, given a set of rules, generates code for a complex event processing application for which it is able to determine time bounds on the response of this application to a set of supported events. In particular, the tool helps determine a time bound for the execution time of the code corresponding to each rule. Many Service Oriented Architecture (SOA) applications, in domains such as financial services, manufacturing, gaming and military/aerospace, have real-time performance requirements. We present real-life industry use cases from these domains as motivation for the potential benefit in developing real-time complex event processing applications. In support of a feasibility argument for the proposed approach we present some preliminary experimental results obtained on a partially implemented tool.