Online social networks, including Twitter, have expanded in both scale and diversity of content, which has created significant challenges to the average user. These challenges include finding relevant information on a topic and building social ties with like-minded individuals. The fundamental question addressed by this thesis is if an individual can leverage social network to search for information that is relevant to him or her. We propose to answer this question by developing computational algorithms that analyze a user's social network. The features of the social network we analyze include the network topology and member communications of a specific user's social network. Determining the "social value" of one's contacts is a valuable outcome of this research. The algorithms we developed were tested on Twitter, which is an extremely popular social network. Twitter was chosen due to its popularity and a majority of the communications artifacts on Twitter is publically available. In this work, the social network of a user refers to the "following relationship" social network. Our algorithm is not specific to Twitter, and is applicable to other social networks, where the network topology and communications are accessible. My approaches are as follows. For a user interested in using the system, I first determine the immediate social network of the user as well as the social contacts for each person in this network. Afterwards, I establish and extend the social network for each user. For each member of the social network, their tweet data are analyzed and represented by using a word distribution. To accomplish this, I use WordNet, a popular lexical database, to determine semantic similarity between two words. My mechanism of search combines both communication distance between two users and social relationships to determine the search results. Additionally, I developed a search interface, where a user can interactively query the system. I conducted preliminary user study to evaluate the quality and utility of my method and system against several baseline methods, including the default Twitter search. The experimental results from the user study indicate that my method is able to find relevant people and identify valuable contacts in one's social circle based on the query. The proposed system outperforms baseline methods in terms of standard information retrieval metrics.

This thesis investigates the role of activity visualization tools in increasing group awareness at the workspace. Today, electronic calendaring tools are widely used in the workplace. The primary function is to enable each person maintain a work schedule. They also are used to schedule meetings and share work details when appropriate. However, a key limitation of current tools is that they do not enable people in the workplace to understand the activity of the group as a whole. A tool that increases group awareness would promote reflection; it would enable thoughtful engagement with one's co-workers. I have developed two tools: the first tool enables the worker to examine detailed task information of one's own tasks, within the context of his/her peers' anonymized task data. The second tool is a public display to promote group reflection. I have used an iterative design methodology to refine the tools. I developed ActivityStream desktop tool that enables users to examine the detailed information of their own activities and the aggregate information of other peers' activities. ActivityStream uses a client-server architecture. The server collected activity data from each user by parsing RSS feeds associated with their preferred online calendaring and task management tool, on a daily basis. The client software displays personalized aggregate data and user specific tasks, including task types. The client display visualizes the activity data at multiple time scales. The activity data for each user is represented though discrete blocks; interacting with the block will reveal task details. The activity of the rest of the group is anonymized and aggregated. ActivityStream visualizes the aggregated data via Bezier curves. I developed ActivityStream public display that shows a group people's activity levels change over time to promote group reflection. In particular, the public display shows the anonymized task activity data, over the course of one year. The public display visualizes data for each user using a Bezier curve. The display shows data from all users simultaneously. This representation enables users to reflect on the relationships across the group members, over the course of one year. The survey results revealed that users are more aware of their peers' activities in the workspace.

Advances in the area of ubiquitous, pervasive and wearable computing have resulted in the development of low band-width, data rich environmental and body sensor networks, providing a reliable and non-intrusive methodology for capturing activity data from humans and the environments they inhabit. Assistive technologies that promote independent living amongst elderly and individuals with cognitive impairment are a major motivating factor for sensor-based activity recognition systems. However, the process of discerning relevant activity information from these sensor streams such as accelerometers is a non-trivial task and is an on-going research area. The difficulty stems from factors such as spatio-temporal variations in movement patterns induced by different individuals and contexts, sparse occurrence of relevant activity gestures in a continuous stream of irrelevant movements and the lack of real-world data for training learning algorithms. This work addresses these challenges in the context of wearable accelerometer-based simple activity and gesture recognition. The proposed computational framework utilizes discriminative classifiers for learning the spatio-temporal variations in movement patterns and demonstrates its effectiveness through a real-time simple activity recognition system and short duration, non- repetitive activity gesture recognition. Furthermore, it proposes adaptive discriminative threshold models trained only on relevant activity gestures for filtering irrelevant movement patterns in a continuous stream. These models are integrated into a gesture spotting network for detecting activity gestures involved in complex activities of daily living. The framework addresses the lack of real world data for training, by using auxiliary, yet related data samples for training in a transfer learning setting. Finally the problem of predicting activity tasks involved in the execution of a complex activity of daily living is described and a solution based on hierarchical Markov models is discussed and evaluated.