On top of an enterprise social platform, we are building a smart social QA system that automatically routes questions to suitable employees who are willing, able, and ready to provide answers. Due to a lack of social QA history (training data) to start with, in this paper, we present an optimization-based approach that recommends both top-matched active (seed) and inactive (prospect) answerers for a given question. Our approach includes three parts. First, it uses a predictive model to find top-ranked seed answerers by their fitness, including their ability and willingness, to answer a question. Second, it uses distance metric learning to discover prospects most similar to the seeds identified in the first step. Third, it uses a constraint-based approach to balance the selection of both seeds and prospects identified in the first two steps. As a result, not only does our solution route questions to top-matched active users, but it also engages inactive users to grow the pool of answerers. Our real-world experiments that routed 114 questions to 684 people identified from 400,000+ employees included 641 prospects (93.7%) and achieved about 70% answering rate with 83% of answers received a lot/full confidence.

Various methods have been proposed to help find answerers for a question in QA communities, but almost all work heavily depends on users' QA history. In this paper we seek to investigate the feasibility of leveraging users' non-QA social activities as a way of gaining insight into their question answering behavior. We collected data of 4,484 users on a QA community in an enterprise and examined the relationship of their QA behaviors and non-QA activities supported by other social tools. We found that the two sets of behavioral indicators are significantly correlated. The top user group for non-QA activities outperformed lower groups in both number and quality of the answers. Regression analysis showed involvement in personal blog, micro-blog and forum and replying and recommending behaviors in non-QA communities were predictive of a user's likelihood of answering questions. Online observations provided a qualitative understanding. Design implications and future work were discussed.

In this paper, we present a case study of MoCo Blogs, a publicly accessible, corporate blogging site, which is hosted by a large telecommunication company in China. We study the design and operation of the site from two aspects: (1) how community owners/administrators guide and coordinate blogger activities to meet the company's business goals; and (2) how participants respond to management guidance and act on their own incentives. Through the analysis of three data sources: 17-month activity logs with 1,046 bloggers and 10,291 blog entries, 4-month online participatory observation, and in-depth interviews, we present two key findings. First, we describe three key operational strategies employed by the site administrative team to encourage user contributions and manage content across the organizational boundaries. Second, we examine how these strategies directly influence user participation. Finally, we discuss the implications of our findings on an organization's roles in corporate social communities.

In this paper we feature a set of research projects done at several IBM Research laboratories across the world. The work featured here focuses on the topic of smart social collaboration, which studies, designs, and develops social collaboration principles and technologies that can help customize and enhance existing social collaboration tools to suit specific user needs, including cultural, business, and personal needs.

Existing studies of online social communities mainly focus on communities in the United States. Since Chinese social beliefs and behaviors largely differ from that of Americans, we hypothesize that Chinese online communities also greatly differ from their U.S. counterparts. In particular, we believe that Chinese online communities must balance management control and individual autonomy to accommodate both Chinese tradition and the social nature of online societies. In this paper, we present three studies to test our hypothesis. First, we use a structured observation (Study I) to examine community governance practices of 32 Chinese and American social sites. Based on the identified community governance practices, we use a cross-cultural survey of 208 Chinese and Americans (Study II) to learn about their behavior and attitude toward these practices. Finally, we interview 38 Chinese users (Study III) to help us further understand how Chinese online communities balance the needs of management and users. Not only do the studies confirm our hypothesis, but they also help us abstract two key design implications of social software to meet the needs of Chinese.

Real-time transcription has been shown to be valuable in facilitating non-native speakers' comprehension in real-time communication. Automated speech recognition (ASR) technology is a critical ingredient for its practical deployment. This paper presents a series of studies investigating how the quality of transcripts generated by an ASR system impacts user comprehension and subjective evaluation. Experiments are first presented comparing performance across three different transcription conditions: no transcript, a perfect transcript, and a transcript with Word Error Rate (WER) =20%. We found 20% WER was the most likely critical point for transcripts to be just acceptable and useful. Then we further examined a lower WER of 10% (a lower bound for today's state-of-the-art systems) employing the same experimental design. The results indicated that at 10% WER comprehension performance was significantly improved compared to the no-transcript condition. Finally, implications for further system development and design are discussed.

We performed an empirical study to understand the relative contributions of real-time transcription to a non-native speaker's comprehension in audio/video meetings. 48 participants were assigned to 2 presentation modes (audio, audio+video) and 3 transcription modes (no transcript, real-time transcripts in the streaming mode, transcripts with all past records) in a 3x2 factorial experimental design. The results suggest that comprehension can be significantly improved for both audio and audio+video conditions when real-time transcription is provided. Also, the participants reported positive subjective responses to the presence of real-time transcription in terms of usefulness, preference, and willingness to use such a feature if provided. No cognitive load issues were reported by the participants in the ability to synthesize across modalities. Implications for system development and design, as well as future work utilizing automation speech recognition to provide the transcripts are discussed.