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.

Software development has become a cognitive and collaborative knowledge-based endeavor where developers and organizations, faced with a variety of challenges and an increased demand for extensive knowledge support, push the boundaries of existing tools and work practices. Researchers and industry professionals have spent years studying collaborative work and communication media, however, the landscape of social media is rapidly changing. Thus, instead of trying to model the use of specific technologies and communication media, I seek to model the knowledge-building process itself. Doing so will not only allow us to understand specific tool and communication media use, but whole ecosystems of technologies and their impact on software development and knowledge work, revealing aspects not only unique to specific tools, but also aspects about the combination of technologies. In this dissertation, I describe the empirical studies I conducted aimed to understand social and communication media use in software development and knowledge curation within developer communities. An important part of the thesis is an additional qualitative meta-synthesis of these studies. My meta-analysis has led to a model of software development as a knowledge building process, and a theoretical framework: I describe this newly formed framework and how it is grounded in empirical work, and demonstrate how my primary studies led to its creation. My conceptualization of knowledge building withing software development and the proposed framework provide the research community with the means to pursue a deeper understanding of software development and contemporary knowledge work. I believe that this framework can serve as a basis for a theory of knowledge building in software development, shedding light on knowledge flow, knowledge productivity, and knowledge management. / Graduate

<p>The availability of abundant multimodal data, including textual, visual, and sensor-based information, holds the potential to improve decision-making in diverse domains. Extracting data-driven decision-making information from heterogeneous and changing datasets in real-world data-centric applications requires achieving complementary functionalities of multimodal data integration, knowledge extraction and mining, situationally-aware data recommendation to different users, and uncertainty management in the open-world setting. To achieve a system that encompasses all of these functionalities, several challenges need to be effectively addressed: (1) How to represent and analyze heterogeneous source contents and application context for multimodal data recommendation? (2) How to predict and fulfill current and future needs as new information streams in without user intervention? (3) How to integrate disconnected data sources and learn relevant information to specific mission needs? (4) How to scale from processing petabytes of data to exabytes? (5) How to deal with uncertainties in open-world that stem from changes in data sources and user requirements?</p> <p><br></p> <p>This dissertation tackles these challenges by proposing novel frameworks, learning-based data integration and retrieval models, and algorithms to empower decision-makers to extract valuable insights from diverse multimodal data sources. The contributions of this dissertation can be summarized as follows: (1) We developed SKOD, a novel multimodal knowledge querying framework that overcomes the data representation, scalability, and data completeness issues while utilizing streaming brokers and RDBMS capabilities with entity-centric semantic features as an effective representation of content and context. Additionally, as part of the framework, a novel text attribute recognition model called HART was developed, which leveraged language models and syntactic properties of large unstructured texts. (2) In the SKOD framework, we incrementally proposed three different approaches for data integration of the disconnected sources from their semantic features to build a common knowledge base with the user information need: (i) EARS: A mediator approach using schema mapping of the semantic features and SQL joins was proposed to address scalability challenges in data integration; (ii) FemmIR: A data integration approach for more susceptible and flexible applications, that utilizes neural network-based graph matching techniques to learn coordinated graph representations of the data. It introduces a novel graph creation approach from the features and a novel similarity metric among data sources; (iii) WeSJem: This approach allows zero-shot similarity matching and data discovery by using contrastive learning<br> to embed data samples and query examples in a high-dimensional space using features as a novel source of supervision instead of relevance labels. (3) Finally, to manage uncertainties in multimodal data management for open-world environments, we characterized novelties in multimodal information retrieval based on data drift. Moreover, we proposed a novelty detection and adaptation technique as an augmentation to WeSJem.<br> </p> <p>The effectiveness of the proposed frameworks, models, and algorithms was demonstrated<br> through real-world system prototypes that solved open problems requiring large-scale human<br> endeavors and computational resources. Specifically, these prototypes assisted law enforcement officers in automating investigations and finding missing persons.<br> </p>

<p dir="ltr">In the dynamic realm of Artificial Intelligence (AI), this study explores the multifaceted landscape of Trustworthy AI with a dedicated focus on achieving both explainability and acceptance. The research addresses the evolving dynamics of AI, emphasizing the essential role of human involvement in shaping its trajectory.</p><p dir="ltr">A primary contribution of this work is the introduction of a novel "Trustworthy Explainability Acceptance Metric", tailored for the evaluation of AI-based systems by field experts. Grounded in a versatile distance acceptance approach, this metric provides a reliable measure of acceptance value. Practical applications of this metric are illustrated, particularly in a critical domain like medical diagnostics. Another significant contribution is the proposal of a trust-based security framework for 5G social networks. This framework enhances security and reliability by incorporating community insights and leveraging trust mechanisms, presenting a valuable advancement in social network security.</p><p dir="ltr">The study also introduces an artificial conscience-control module model, innovating with the concept of "Artificial Feeling." This model is designed to enhance AI system adaptability based on user preferences, ensuring controllability, safety, reliability, and trustworthiness in AI decision-making. This innovation contributes to fostering increased societal acceptance of AI technologies. Additionally, the research conducts a comprehensive survey of foundational requirements for establishing trustworthiness in AI. Emphasizing fairness, accountability, privacy, acceptance, and verification/validation, this survey lays the groundwork for understanding and addressing ethical considerations in AI applications. The study concludes with exploring quantum alternatives, offering fresh perspectives on algorithmic approaches in trustworthy AI systems. This exploration broadens the horizons of AI research, pushing the boundaries of traditional algorithms.</p><p dir="ltr">In summary, this work significantly contributes to the discourse on Trustworthy AI, ensuring both explainability and acceptance in the intricate interplay between humans and AI systems. Through its diverse contributions, the research offers valuable insights and practical frameworks for the responsible and ethical deployment of AI in various applications.</p>

<p dir="ltr">Humans are social beings and throughout our evolution we have survived and thrived thanks to our ability to cooperate [7]. Overcoming our current societal challenges from sustainability and energy conservation [8] to democracy, public health, and community building [9] will all require our continued cooperation. Yet, many of these present us with a dilemma where our short-term personal goals are at odds with the collective long-term benefits. For example, many of us listen NPR radio but never make a donation to help cover its operational costs. The success of cooperation during such dilemmatic situations often depends on communication, reward and punishment structures, social norms and cues [10], [11], [12], [13]. But how to encourage cooperation online where social cues are not readily available?</p><p dir="ltr">Accelerated by the COVID-19 pandemic and the prevalence of digital technologies, cooperation among individuals increasingly happens online where data-based feedback supports our decisions. Problematically, people online are often not only remote and asynchronous, but often also anonymous, which has resulted in de-individuation and antinormative behavior [14]. Social data, information that users share about themselves via digital technologies, may offer opportunities for social feedback design that affords perceptions of social cohesion and may support successful cooperation online.</p><p dir="ltr">This dissertation seeks to answer the normative question of how to design for cooperation in social data feedback charts in dilemmatic situations online. I conducted mixed methods design research by combining theory-driven design with a series of controlled experiments on Amazon Mechanical Turk to understand the perceptual and behavioral effects of visually unifying social data feedback charts. To achieve this, I mapped the design space for home energy feedback (<i>Chapter 2</i>) to guide my iterative and user-centered theorizing about how visual unity in social feedback charts might prime viewers with unified group perceptions (<i>Chapter 3</i>). I then validated my theorizing with controlled perceptual (<i>Chapter 4</i>) and decision experiments (<i>Chapter 5</i>).</p><p dir="ltr">The triangulated results offer evidence for visually unifying cues in feedback charts affecting social data interpretation (<i>Chapter 4</i>) and cooperation online (<i>Chapter 5</i>). Two visual properties: data point <i>proximity</i> and <i>enclosure</i> -, trigger variable levels of perceivable social unity that play a partial role in participants’ decision to cooperate in a non-monetary social dilemma situation online. I discuss the implications for future research and design (<i>Chapter 6</i>).</p>