Sanjay Kairam | Stanford University (original) (raw)
Papers by Sanjay Kairam
Abstract Visual methods for supporting the characterization, comparison, and classification of la... more Abstract Visual methods for supporting the characterization, comparison, and classification of large networks remain an open challenge. Ideally, such techniques should surface useful structural features--such as effective diameter, small-world properties, and structural holes--not always apparent from either summary statistics or typical network visualizations.
We describe the collaborative use of word tree visualizations, EchoTrees, to facilitate face-to-f... more We describe the collaborative use of word tree visualizations, EchoTrees, to facilitate face-to-face, and remote communication with speech and movement impaired individuals. EchoTree is designed to bridge the inevitable conversational dead space while the impaired person uses assistive technolo-gies to generate written, or artificially spoken sentences. Visualizations that guess multiple conversational directions in which the impaired person might be headed keep conversation partners engaged.
Abstract We propose a user model to support personalized learning paths through online material. ... more Abstract We propose a user model to support personalized learning paths through online material. Our approach is a variant of student modeling using the computer tutoring concept of knowledge tracing. Knowledge tracing involves representing the knowledge required to master a domain, and, from traces of online user behavior, diagnosing user knowledge states as a profile over those elements. The user model is induced from documents tagged by an expert in a social tagging system.
Online social networks have become indispensable tools for information-sharing, but existing ‘all... more Online social networks have become indispensable tools for information-sharing, but existing ‘all-or-nothing’ models for sharing have made it difficult for users to target information to specific parts of their network. In this paper, we study Google+, which enables users to selectively share content with specific ‘Circles’ of people. Through a combination of log analysis with surveys and interviews, we investigate how active users organize and select audiences for shared content. We find that users frequently engaged in selective sharing, creating circles to manage content across particular life facets, ties of varying strength, and interest-based groups. Motivations to share spanned personal and informational reasons, and users frequently weighed limiting factors (such as privacy, relevance, and social norms) against the desire to reach a large audience. Our work identifies the nuances and design implications associated with the move towards a selective sharing model for social networks.
We pose a fundamental question in understanding how to identify and design successful communities... more We pose a fundamental question in understanding how to identify and design successful communities: What factors predict whether a community will grow and survive in the long term? Social scientists have addressed this question extensively by analyzing offline groups which endeavor to attract new members, such as social movements, finding that new individuals are influenced strongly by their ties to members of the group. As a result, prior work on the growth of communities has treated growth primarily as a diffusion processes, leading to findings about group evolution which can be difficult to explain. The proliferation of online social networks and communities, however, has created new opportunities to study, at a large scale and with very fine resolution, the mechanisms which lead to the formation, growth, and demise of online groups.
In this paper, we analyze data from several thousand online social networks built on the Ning platform with the goal of understanding the factors contributing to the growth and longevity of groups within these networks. Specifically, we investigate the role that two types of growth (growth through diffusion and growth by other means) play during a group’s formative stages from the perspectives of both the individual member and the group. Applying these insights to a population of groups of different ages and sizes, we build a model to classify groups which will grow rapidly over the short-term and long-term. Our model achieves over 79% accuracy in predicting group growth over the following two months and over 78% accuracy in predictions over the following two years. We utilize a similar approach to predict which groups will die within a year. The results of our combined analysis provide insight into how both early non-diffusion growth and a complex set of network constraints appear to contribute to the initial and continued growth and success of groups within social networks. Finally we discuss implications of this work for the design, maintenance, and analysis of online communities.
Proceedings of the 28th …, Jan 1, 2010
Information Processing & Management, Jan 1, 2009
Proceedings of the 27th international …, Jan 1, 2009
Abstract To what extent can social interactions augment people's natural search experien... more Abstract To what extent can social interactions augment people's natural search experiences? What factors influence the decision to turn to a friend for help? Our paper presents the preliminary results of a social sensemaking task that begin to address such questions ...
Proceedings of the …, Jan 1, 2010
Abstract Visual methods for supporting the characterization, comparison, and classification of la... more Abstract Visual methods for supporting the characterization, comparison, and classification of large networks remain an open challenge. Ideally, such techniques should surface useful structural features--such as effective diameter, small-world properties, and structural holes--not always apparent from either summary statistics or typical network visualizations.
We describe the collaborative use of word tree visualizations, EchoTrees, to facilitate face-to-f... more We describe the collaborative use of word tree visualizations, EchoTrees, to facilitate face-to-face, and remote communication with speech and movement impaired individuals. EchoTree is designed to bridge the inevitable conversational dead space while the impaired person uses assistive technolo-gies to generate written, or artificially spoken sentences. Visualizations that guess multiple conversational directions in which the impaired person might be headed keep conversation partners engaged.
Abstract We propose a user model to support personalized learning paths through online material. ... more Abstract We propose a user model to support personalized learning paths through online material. Our approach is a variant of student modeling using the computer tutoring concept of knowledge tracing. Knowledge tracing involves representing the knowledge required to master a domain, and, from traces of online user behavior, diagnosing user knowledge states as a profile over those elements. The user model is induced from documents tagged by an expert in a social tagging system.
Online social networks have become indispensable tools for information-sharing, but existing ‘all... more Online social networks have become indispensable tools for information-sharing, but existing ‘all-or-nothing’ models for sharing have made it difficult for users to target information to specific parts of their network. In this paper, we study Google+, which enables users to selectively share content with specific ‘Circles’ of people. Through a combination of log analysis with surveys and interviews, we investigate how active users organize and select audiences for shared content. We find that users frequently engaged in selective sharing, creating circles to manage content across particular life facets, ties of varying strength, and interest-based groups. Motivations to share spanned personal and informational reasons, and users frequently weighed limiting factors (such as privacy, relevance, and social norms) against the desire to reach a large audience. Our work identifies the nuances and design implications associated with the move towards a selective sharing model for social networks.
We pose a fundamental question in understanding how to identify and design successful communities... more We pose a fundamental question in understanding how to identify and design successful communities: What factors predict whether a community will grow and survive in the long term? Social scientists have addressed this question extensively by analyzing offline groups which endeavor to attract new members, such as social movements, finding that new individuals are influenced strongly by their ties to members of the group. As a result, prior work on the growth of communities has treated growth primarily as a diffusion processes, leading to findings about group evolution which can be difficult to explain. The proliferation of online social networks and communities, however, has created new opportunities to study, at a large scale and with very fine resolution, the mechanisms which lead to the formation, growth, and demise of online groups.
In this paper, we analyze data from several thousand online social networks built on the Ning platform with the goal of understanding the factors contributing to the growth and longevity of groups within these networks. Specifically, we investigate the role that two types of growth (growth through diffusion and growth by other means) play during a group’s formative stages from the perspectives of both the individual member and the group. Applying these insights to a population of groups of different ages and sizes, we build a model to classify groups which will grow rapidly over the short-term and long-term. Our model achieves over 79% accuracy in predicting group growth over the following two months and over 78% accuracy in predictions over the following two years. We utilize a similar approach to predict which groups will die within a year. The results of our combined analysis provide insight into how both early non-diffusion growth and a complex set of network constraints appear to contribute to the initial and continued growth and success of groups within social networks. Finally we discuss implications of this work for the design, maintenance, and analysis of online communities.
Proceedings of the 28th …, Jan 1, 2010
Information Processing & Management, Jan 1, 2009
Proceedings of the 27th international …, Jan 1, 2009
Abstract To what extent can social interactions augment people's natural search experien... more Abstract To what extent can social interactions augment people's natural search experiences? What factors influence the decision to turn to a friend for help? Our paper presents the preliminary results of a social sensemaking task that begin to address such questions ...
Proceedings of the …, Jan 1, 2010