The current problem-solving paradigm for software developers revolves around using a search engine to find knowledge about the problem and its solutions. This approach provides the developer with search results that are only restricted by the context of the keywords they used to search. Problem-Resolution Dissemination (PRD) is a system and method for collecting, filtering, storing and distributing knowledge that users discover and access when solving a problem. The method involves an agent running on a user's (Alice???s) browsing client which is enabled when Alice is solving a problem. After Alice indicates that she has solved the problem, the agent will collect all web pages visited when solving the problem and filter out the pages that are not relevant. Pointers to the remaining pages (URIs) are tagged with Alice???s identity and stored in the central repository. When another user (Bob) attempts to solve the same problem, the above repository is queried based on Bob's social context. This social context is defined by Bob as a group of other users who have one of three trust levels: team, peer or community. The results are displayed by ranking them within each of the above contexts. In the event that no results are relevant to the Bob, he has the option of following traditional problem solving approaches. When Bob has solved his problem, the web pages he visited are added to the repository and made available to future users. In this manner, PRD incorporates relationships and previous experiences to improve the relevancy of results and thus efficiency in problem solving.

The current problem-solving paradigm for software developers revolves around using a search engine to find knowledge about the problem and its solutions. This approach provides the developer with search results that are only restricted by the context of the keywords they used to search. Problem-Resolution Dissemination (PRD) is a system and method for collecting, filtering, storing and distributing knowledge that users discover and access when solving a problem. The method involves an agent running on a user's (Alice’s) browsing client which is enabled when Alice is solving a problem. After Alice indicates that she has solved the problem, the agent will collect all web pages visited when solving the problem and filter out the pages that are not relevant. Pointers to the remaining pages (URIs) are tagged with Alice’s identity and stored in the central repository. When another user (Bob) attempts to solve the same problem, the above repository is queried based on Bob's social context. This social context is defined by Bob as a group of other users who have one of three trust levels: team, peer or community. The results are displayed by ranking them within each of the above contexts. In the event that no results are relevant to the Bob, he has the option of following traditional problem solving approaches. When Bob has solved his problem, the web pages he visited are added to the repository and made available to future users. In this manner, PRD incorporates relationships and previous experiences to improve the relevancy of results and thus efficiency in problem solving.

Tagging is widely adopted in so-called "collaborative-tagging" systems which are one of the Web 2.0 applications that have achieved lots of attention lately. They provide services for users to store, manage and search web resources with the help of freely chosen keywords, called "tags". Because of the high-volume usage of these systems and the annotations that users provide by their tags, these systems are regarded as good targets for disciplines like knowledge discovery. Roughly, two lines of research have been pursued so far on collaborative tagging: to study the structure of tags and to study their functionality in web search. In this research we investigated tagging structures in a popular collaborative-tagging system, called del.icio.us, by focusing on the relations of "tags", "users" and "web resources", three main components of any collaborative-tagging system. Particularly we are interested in finding whether there are social structures that could be used to increase the usability of these systems for content retrieval and navigation. Our results show that people mainly use tags for their own informational needs which are personal rather than social. Any social structure or communities around tags and users is rare and weak which suggests that collaborative tagging has not added much to personal bookmarking. However, we show some regularities in tagging behavior that could be utilized for user experience improvement. / Thesis (Master, Computing) -- Queen's University, 2008-12-04 14:34:37.537

While technologically Computer Supported Collaborative Learning (CSCL) systems have been considerably improved, previous studies have shown that the social aspect of CSCL is often neglected or assumed to happen automatically just by creating such virtual learning environments. Several studies on the other hand showed sociability of CSCL environments strongly relate to online learning enjoyment and effectiveness of learning. Social interactions in CSCL can be broadly categorized as pedagogical/on-task and non-pedagogical/non-task interactions. Accordingly, this thesis investigates the non-pedagogical sociability of CSCL environments and primarily demonstrates that non-pedagogical/non-task interactions do occur in these environments. In addition, this thesis operationalizes the notion of non-pedagogical sociability of CSCL environments and determines factors that impact on it. A multi method approach for data collection and analysis is used. Results of a content analysis extend the Transcript Analysis Tool (TAT) instrument, the tool used for the content analysis, and show the presence and pattern of a substantial amount of nonpedagogical social exchanges occur in both virtual task-related as well as non-taskrelated spaces. The findings from the survey data, on the other hand, support the SIP theory and SIDE theory predictions and revealed that the sense of cohesion and awareness of others significantly impact on the non-pedagogical sociability of CSCL. Furthermore, the study demonstrates that the perception of self-representation and perception of compatibility affect the sense of cohesion and awareness of others and indirectly contribute to the perceived non-pedagogical sociability of the environment. The findings of this thesis can be used in future research for investigating the relationship between the non-pedagogical sociability of CSCL and other CSCL factors. It also provides CSCL lecturers and facilitators with a conceptual model by which sociability can be explicitly addressed in their course planning and delivery processes. From a practical point of view, this study develops and validates an instrument that guides required changes in the CSCL for improving the non-pedagogical social functionality of the environment. The findings also stress the importance of virtual spaces in CSCL design exercises in order to provide opportunities for students to have non-task discussions and to reflect on their own and others??? experiences and ideas.

While technologically Computer Supported Collaborative Learning (CSCL) systems have been considerably improved, previous studies have shown that the social aspect of CSCL is often neglected or assumed to happen automatically just by creating such virtual learning environments. Several studies on the other hand showed sociability of CSCL environments strongly relate to online learning enjoyment and effectiveness of learning. Social interactions in CSCL can be broadly categorized as pedagogical/on-task and non-pedagogical/non-task interactions. Accordingly, this thesis investigates the non-pedagogical sociability of CSCL environments and primarily demonstrates that non-pedagogical/non-task interactions do occur in these environments. In addition, this thesis operationalizes the notion of non-pedagogical sociability of CSCL environments and determines factors that impact on it. A multi method approach for data collection and analysis is used. Results of a content analysis extend the Transcript Analysis Tool (TAT) instrument, the tool used for the content analysis, and show the presence and pattern of a substantial amount of nonpedagogical social exchanges occur in both virtual task-related as well as non-taskrelated spaces. The findings from the survey data, on the other hand, support the SIP theory and SIDE theory predictions and revealed that the sense of cohesion and awareness of others significantly impact on the non-pedagogical sociability of CSCL. Furthermore, the study demonstrates that the perception of self-representation and perception of compatibility affect the sense of cohesion and awareness of others and indirectly contribute to the perceived non-pedagogical sociability of the environment. The findings of this thesis can be used in future research for investigating the relationship between the non-pedagogical sociability of CSCL and other CSCL factors. It also provides CSCL lecturers and facilitators with a conceptual model by which sociability can be explicitly addressed in their course planning and delivery processes. From a practical point of view, this study develops and validates an instrument that guides required changes in the CSCL for improving the non-pedagogical social functionality of the environment. The findings also stress the importance of virtual spaces in CSCL design exercises in order to provide opportunities for students to have non-task discussions and to reflect on their own and others??? experiences and ideas.

While technologically Computer Supported Collaborative Learning (CSCL) systems have been considerably improved, previous studies have shown that the social aspect of CSCL is often neglected or assumed to happen automatically just by creating such virtual learning environments. Several studies on the other hand showed sociability of CSCL environments strongly relate to online learning enjoyment and effectiveness of learning. Social interactions in CSCL can be broadly categorized as pedagogical/on-task and non-pedagogical/non-task interactions. Accordingly, this thesis investigates the non-pedagogical sociability of CSCL environments and primarily demonstrates that non-pedagogical/non-task interactions do occur in these environments. In addition, this thesis operationalizes the notion of non-pedagogical sociability of CSCL environments and determines factors that impact on it. A multi method approach for data collection and analysis is used. Results of a content analysis extend the Transcript Analysis Tool (TAT) instrument, the tool used for the content analysis, and show the presence and pattern of a substantial amount of nonpedagogical social exchanges occur in both virtual task-related as well as non-taskrelated spaces. The findings from the survey data, on the other hand, support the SIP theory and SIDE theory predictions and revealed that the sense of cohesion and awareness of others significantly impact on the non-pedagogical sociability of CSCL. Furthermore, the study demonstrates that the perception of self-representation and perception of compatibility affect the sense of cohesion and awareness of others and indirectly contribute to the perceived non-pedagogical sociability of the environment. The findings of this thesis can be used in future research for investigating the relationship between the non-pedagogical sociability of CSCL and other CSCL factors. It also provides CSCL lecturers and facilitators with a conceptual model by which sociability can be explicitly addressed in their course planning and delivery processes. From a practical point of view, this study develops and validates an instrument that guides required changes in the CSCL for improving the non-pedagogical social functionality of the environment. The findings also stress the importance of virtual spaces in CSCL design exercises in order to provide opportunities for students to have non-task discussions and to reflect on their own and others??? experiences and ideas.

One in seven people in the world use online social networking for a variety of purposes -- to keep in touch with friends and family, to share special occasions, to broadcast announcements, and more. The majority of society has been bought into this new era of communication technology, which allows everyone on the internet to share information with friends. Since social networking has rapidly become a main form of communication, holes in privacy have become apparent. It has come to the point that the whole concept of sharing information requires restructuring. No longer are online social networks simply technology available for a niche market; they are in use by all of society. Thus it is important to not forget that a sense of privacy is inherent as an evolutionary by-product of social intelligence. In any context of society, privacy needs to be a part of the system in order to help users protect themselves from others. This dissertation attempts to address the lack of privacy management in online social networks by designing models which understand the social science behind how we form social groups and share information with each other. Social relationship strength was modeled using activity patterns, vocabulary usage, and behavioral patterns. In addition, automatic configuration for default privacy settings was proposed to help prevent new users from leaking personal information. This dissertation aims to mobilize a new era of social networking that understands social aspects of human network, and uses that knowledge to honor users' privacy.

Email correspondence has become the predominant method of communication for businesses. If not for the inherent privacy concerns, this electronically searchable data could be used to better understand how employees interact. After the Enron dataset was made available, researchers were able to provide great insight into employee behaviors based on the available data despite the many challenges with that dataset. The work in this thesis demonstrates a suite of methods to an appropriately anonymized academic email dataset created from volunteers' email metadata. This new dataset, from an internal email server, is first used to validate feature extraction and machine learning algorithms in order to generate insight into the interactions within the center. Based solely on email metadata, a random forest approach models behavior patterns and predicts employee job titles with $96%$ accuracy. This result represents classifier performance not only on participants in the study but also on other members of the center who were connected to participants through email. Furthermore, the data revealed relationships not present in the center's formal operating structure. The culmination of this work is an organic organizational chart, which contains a fuller understanding of the center's internal structure than can be found in the official organizational chart. / Master of Science

In today's digital age, social media platforms have become powerful tools for communication, enabling users to express their opinions while also exposing them to various forms of hateful speech and content. While prior research has often focused on the efficacy of online counterspeech, little is known about peoples' motivations for engaging in it. Based on a survey of 458 U.S. participants, we develop and validate a multi-item scale for understanding counterspeech motivations, revealing that differing motivations impact counterspeech engagement between those that do and not find counterspeech to be an effective mechanism for counteracting online hate. Additionally, our analysis explores peoples' perceived effectiveness of their self-written counterspeech to hateful posts, influenced by individual motivations to engage in counterspeech and demographic factors. Finally, we examine peoples' willingness to employ AI assistance, such as ChatGPT, in their counterspeech writing efforts. Our research provides insight into the factors that influence peoples' online counterspeech activity and perceptions, including the potential role of AI assistance in countering online hate. / Master of Science / In today's digital age, social media platforms have become powerful tools for communication, enabling users to express their opinions while also exposing them to various forms of hateful speech and content. In addition to content moderation, counterspeech, or direct responses aimed at undermining hateful speech, is a tool that is being explored by organizations to counteract online hate, as it has been shown to prevent "platform hopping" while also promoting free speech. While prior research has primarily focused on the effectiveness of various types of counterspeech, little is known about what motivates people to engage in it. Based on a survey of 458 U.S. participants, we develop and validate a multi-item scale for understanding counterspeech motivations, revealing that differing motivations impact counterspeech engagement between those that do and not find counterspeech to be an effective mechanism for counteracting online hate. Additionally, our analysis explores peoples' perceived effectiveness of their counterspeech, influenced by individual motivations to engage in counterspeech and demographic factors. Finally, we examine peoples' willingness to employ AI assistance, such as ChatGPT, in their counterspeech writing efforts. Our research provides insight into the factors that influence peoples' online counterspeech activity and perceptions, including the potential role of AI assistance in countering online hate.

This thesis contributes to a better understanding of social media designs for more civil conversations online. I first demonstrated that people disengage from social media interactions when they encounter uncivil behavior from friends. To find alternative designs for social media that are more civil, I evaluated novel social interaction techniques. To do this, I designed a six-phase framework for prototyping social interactions called piggyback prototyping; and an algorithmic probe study methodology to include participants in the development of social curation algorithms. I built a piggyback prototype that modifies the civility on Facebook by highlighting positive posts in green and hiding impolite posts, and I deployed it as an algorithmic probe with 20 participants. I uncovered ways to improve the algorithm, and I found that participants responded most favorably to having civil posts highlighted. These findings open avenues for future research in designing pro-social platforms.