Businesses have recognized that customers provide value to the firm beyond transactions, and leveraging this value through relationships in social media is a new area of interest for both academics and practitioners. Recent research has investigated how businesses can best manage their online presence on platforms not fully under their control, such as Facebook, YouTube, Instagram, TripAdvisor, and Yelp, among others. This dissertation extends the literature of customer engagement in social media through four contributions. First, we propose a framework that foregrounds the textual artifacts involved in online communication. Second, we develop a novel method for discovering the elements of successful Business to Customer (B2C) messages in online communities. Third, we propose a method, validated through experimentation, for finding critical product feedback in Customer to Customer (C2C) communications. Finally, we demonstrate that a set of novel numerical features can enhance the discovery of product defect mentions in C2C communications. We conclude by proposing a research agenda suggested by the framework that will further enhance our understanding of the complex customer interactions that characterize business in the era of social media. / Ph. D.

The increasing volume and complexity of text data are challenging the cognitive capabilities of expert analysts. Machine learning and crowdsourcing present new opportunities for large-scale sensemaking, but it remains a challenge to model the overall process so that many distributed agents can contribute to suitable components asynchronously and meaningfully. In this work, I explore how to crowdsource sensemaking for intelligence analysis. Specifically, I focus on the complex processes that include developing hypotheses and theories from a raw dataset and iteratively refining the analysis. I first developed Connect the Dots, a web application that implements the concept of "context slices" and supports novice crowds in building relationship networks for exploratory analysis. Then I developed CrowdIA, a software platform that implements the entire crowd sensemaking pipeline and the context slicing for each step, to enable unsupervised crowd sensemaking. Using the pipeline as a testbed, I probed the errors and bottlenecks in crowdsourced sensemaking,and suggested design recommendations for integrated crowdsourcing systems. Building on these insights and to support iterative crowd sensemaking, I developed the concept of "crowd auditing" in which an auditor examines a pipeline of crowd analyses and diagnoses the problems to steer future refinement. I explored the design space to support crowd auditing and developed CrowdTrace, a crowd auditing tool that enables novice auditors to effectively identify the important problems with the crowd analysis and create microtasks for crowd workers to fix the problems.The core contributions of this work include a pipeline that enables distributed crowd collaboration to holistic sensemaking processes, two novel concepts of "context slices" and "crowd auditing", web applications that support crowd sensemaking and auditing, as well as design implications for crowd sensemaking systems. The hope is that the crowd sensemaking pipeline can serve to accelerate research on sensemaking, and contribute to helping people conduct in-depth investigations of large collections of information. / Doctor of Philosophy / In today's world, we have access to large amounts of data that provide opportunities to solve problems at unprecedented depths and scales. While machine learning offers powerful capabilities to support data analysis, to extract meaning from raw data is cognitively demanding and requires significant person-power. Crowdsourcing aggregates human intelligence, yet it remains a challenge for many distributed agents to collaborate asynchronously and meaningfully. The contribution of this work is to explore how to use crowdsourcing to make sense of the copious and complex data. I first implemented the concept of ``context slices'', which split up complex sensemaking tasks by context, to support meaningful division of work. I developed a web application, Connect the Dots, which generates relationship networks from text documents with crowdsourcing and context slices. Then I developed a crowd sensemaking pipeline based on the expert sensemaking process. I implemented the pipeline as a web platform, CrowdIA, which guides crowds to solve mysteries without expert intervention. Using the pipeline as a testbed, I probed the errors and bottlenecks in crowd sensemaking and provided design recommendations for crowd intelligence systems. Finally, I introduced the concept of ``crowd auditing'', in which an auditor examines a pipeline of crowd analyses and diagnoses the problems to steer a top-down path of the pipeline and refine the crowd analysis. The hope is that the crowd sensemaking pipeline can serve to accelerate research on sensemaking, and contribute to helping people conduct in-depth investigations of large collections of data.

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Artificial intelligence (AI) has made astonishing breakthroughs in recent years and achieved comparable or even better performance compared to humans on many real-world tasks and applications. However, it is still far from reaching human-level intelligence in many ways. Specifically, although AI may take inspiration from neuroscience and cognitive psychology, it is dramatically different from humans in both what it learns and how it learns. Given that current AI cannot learn as effectively and efficiently as humans do, a natural solution is analyzing human learning processes and projecting them into AI design. This dissertation presents three studies that examined cognitive theories and established frameworks to integrate crucial human cognitive learning elements into AI algorithms to build human learning–augmented AI in the context of text analytics. The first study examined compositionality—how information is decomposed into small pieces, which are then recomposed to generate larger pieces of information. Compositionality is considered as a fundamental cognitive process, and also one of the best explanations for humans' quick learning abilities. Thus, integrating compositionality, which AI has not yet mastered, could potentially improve its learning performance. By focusing on text analytics, we first examined three levels of compositionality that can be captured in language. We then adopted design science paradigms to integrate these three types of compositionality into a deep learning model to build a unified learning framework. Lastly, we extensively evaluated the design on a series of text analytics tasks and confirmed its superiority in improving AI's learning effectiveness and efficiency. The second study focused on transfer learning, a core process in human learning. People can efficiently and effectively use knowledge learned previously to solve new problems. Although transfer learning has been extensively studied in AI research and is often a standard procedure in building machine learning models, existing techniques are not able to transfer knowledge as effectively and efficiently as humans. To solve this problem, we first drew on the theory of transfer learning to analyze the human transfer learning process and identify the key elements that elude AI. Then, following the design science paradigm, a novel transfer learning framework was proposed to explicitly capture these cognitive elements. Finally, we assessed the design artifact's capability to improve transfer learning performance and validated that our proposed framework outperforms state-of-the-art approaches on a broad set of text analytics tasks. The two studies above researched knowledge composition and knowledge transfer, while the third study directly addressed knowledge itself by focusing on knowledge structure, retrieval, and utilization processes. We identified that despite the great progress achieved by current knowledge-aware AI algorithms, they are not dealing with complex knowledge in a way that is consistent with how humans manage knowledge. Grounded in schema theory, we proposed a new design framework to enable AI-based text analytics algorithms to retrieve and utilize knowledge in a more human-like way. We confirmed that our framework outperformed current knowledge-based algorithms by large margins with strong robustness. In addition, we evaluated more intricately the efficacy of each of the key design elements. / Doctor of Philosophy / This dissertation presents three studies that examined cognitive theories and established frameworks to integrate crucial human cognitive learning elements into artificial intelligence (AI) algorithm designs to build human learning–augmented AI in the context of text analytics. The first study examined compositionality—how information is decomposed into small pieces, which are then recomposed to generate larger pieces of information. Design science research methodology has been adopted to propose a novel deep learning–based framework that can incorporate three levels of compositionality in language with significantly improved learning performance on a series of text analytics tasks. The second study went beyond that basic element and focused on transfer learning—how humans can efficiently and effectively use knowledge learned previously to solve new problems. Our novel transfer learning framework, which is grounded in the theory of transfer learning, has been validated on a broad set of text analytics tasks with improved learning effectiveness and efficiency. Finally, the third study directly addressed knowledge itself by focusing on knowledge structure, retrieval, and utilization processes. We drew on schema theory and proposed a new design framework to enable AI-based text analytics algorithms to retrieve and utilize knowledge in a more human-like way. Lastly, we confirmed our design's superiority in dealing with knowledge on several common text analytics tasks compared to existing knowledge-based algorithms.

In recent years, the expansion of online media has presented firms with rich and voluminous new datasets with profound business applications. Among these, online reviews provide nuanced details on consumers' interactions with products. Analysis of these reviews has enormous potential, but the enormity of the data and the nature of unstructured text make mining these insights challenging and time-consuming. This paper presents three studies examining this problem and suggesting techniques for automated extraction of vital insights. The first study examines the problem of identifying mentions of safety hazards in online reviews. Discussions of hazards may have profound importance for firms and regulators as they seek to protect consumers. However, as most online reviews do not pertain to safety hazards, identifying this small portion of reviews is a challenging problem. Much of the literature in this domain focuses on selecting "smoke terms," or specific words and phrases closely associated with the mentions of safety hazards. We first examine and evaluate prior techniques to identify these reviews, which incorporate substantial human opinion in curating smoke terms and thus vary in their effectiveness. We propose a new automated method that utilizes a heuristic to curate smoke terms, and we find that this method is far more efficient than the human-driven techniques. Finally, we incorporate consumers' star ratings in our analysis, further improving prediction of safety hazard-related discussions. The second study examines the identification of consumer-sourced innovation ideas and opportunities from online reviews. We build upon a widely-accepted attribute mapping framework from the entrepreneurship literature for evaluating and comparing product attributes. We first adapt this framework for use in the analysis of online reviews. Then, we develop analytical techniques based on smoke terms for automated identification of innovation opportunities mentioned in online reviews. These techniques can be used to profile products as to attributes that affect or have the potential to affect their competitive standing. In collaboration with a large countertop appliances manufacturer, we assess and validate the usefulness of these suggestions, tying together the theoretical value of the attribute mapping framework and the practical value of identifying innovation-related discussions in online reviews. The third study addresses safety hazard monitoring for use cases in which a higher yield of safety hazards detected is desirable. We note a trade-off between the efficiency of hazard techniques described in the first study and the depth of such techniques, as a high proportion of identified records refer to true hazards, but several important hazards may be undetected. We suggest several techniques for handling this trade-off, including alternate objective functions for heuristics and fuzzy term matching, which improve the total yield. We examine the efficacy of each of these techniques and contrast their merits with past techniques. Finally, we test the capability of these methods to generalize to online reviews across different product categories. / Doctor of Philosophy / This dissertation presents three studies that utilize text analytic methods to analyze and derive insights from online reviews. The first study aims to detect distinctive words and phrases particularly prevalent in online reviews that describe safety hazards. This study proposes algorithmic and heuristic methods for identifying words and phrases that are especially common in these reviews, allowing for an automated process to prioritize these reviews for practitioners more efficiently. The second study extends these methods for use in detecting mentions of product innovation opportunities in online reviews. We show that these techniques can used to profile products based on attributes that differentiate them from competition or have the potential to do so in the future. Additionally, we validate that product managers find this attribute profiling useful to their innovation processes. Finally, the third study examines automated safety hazard monitoring for situations in which the yield or total number of safety hazards detected is an important consideration in addition to efficiency. We propose a variety of new techniques for handling these situations and contrast them with the techniques used in prior studies. Lastly, we test these methods across diverse product categories.

The purpose of this dissertation intends to discover as well as categorize safety concern reports in online reviews by using key terms prevalent in sub-categories of safety concerns. This dissertation extends the literature of semi-automatic text classification methodology in monitoring and classifying product quality and service concerns. We develop various text classification methods for finding key concerns across a diverse set of product and service categories. Additionally, we generalize our results by testing the performance of our methodologies on online reviews collected from two different data sources (Amazon product reviews and Facebook hospital service reviews). Stakeholders such as product designers and safety regulators can use the semi-automatic classification procedure to subcategorize safety concerns by injury type and narrative type (Chapter 1). We enhance the text classification approach by proposing a Risk Assessment Model for quality management (QM) professionals, safety regulators, and product designers to allow them to estimate overall risk level of specific products by analyzing consumer-generated content in online reviews (Chapter 2). Monitoring and prioritizing the hazard risk levels of products will help the stakeholders to make appropriate actions on mitigating the risk of product safety. Lastly, the text classification approach discovers and ranks aspects of services that predict overall user satisfaction (Chapter 3). The key service terms are beneficial for healthcare providers to rapidly trace specific service concerns for improving the hospital services. / Doctor of Philosophy / This dissertation extends past studies by examining safety surveillance of online reviews. We examine online reviews reporting specific categories of safety concerns and contrast them with reviews not reporting these specific safety concerns. Business and regulators are benefited in detecting, categorizing, and prioritizing safety concerns across product categories. We use key terms prevalent in domain-related safety concerns for granular analysis of consumer reviews. Secondly, beyond utilizing the key terms to discover specific hazard incidents, safety regulators and manufacturers may use the extended risk assessment framework to estimate the risk severity, risk likelihood, and overall risk level of a specific product. The model could be useful for product safety practitioners in product risk identification and mitigation. Finally, this dissertation identifies the aspects of service quality concerns present in online hospital reviews. This study uses text analytics method by using key terms to detect these specific service concerns and hence determine primary rationales for patient feedback on hospital services. Managerially, this information helps to prioritize the areas in greatest need of improvement of hospital services. Additionally, generating key terms for a particular service attribute aids health care policy makers and providers in rapidly monitoring specific concerns and adjusting policies or resources to better serve patient

Opinion Mining is becoming the fastest growing area to extract useful and insightful information to support decision making. In the age of social media, user’s opinions and discussions have become a highly valuable source to look for users preferences, likes, and dislikes. The industry of wildlife parks (or zoos) is a competitive domain that requires careful analysis of visitor’s opinions to understand and cater for their preferences when it comes to wildlife. In this thesis, an opinion mining approach was proposed and applied on textual posts on the social media platform, Twitter, to extract the popularity, polarity (sentiment), and emotions toward birds and bird types such as owls, sparrows, etc. Then, the thesis provides recommendations based on popularity of birds and bird types and a ranked list of the most desired birds based on consumer emotions toward them. The findings of this thesis can help wildlife parks in the decision-making process on the types of birds to acquire.

After more than two decades, bullying and cyberbullying is still negatively impacting the lives of many of our youth and their families. The prevalence of the phenomenon is widespread and part of the everyday life activities. The impact of cyber aggression and violation can have severe consequences, up to the destruction of lives. While cyberbullying prevention programs exist, not much progress seems to have been made in the effort to combat the phenomenon. This research provides new insights into how to extract information by using existing research and online news articles, with the aim to create new or improve existing cyberbullying prevention efforts. The intent is to inform prevention programs.

Yes / Information management is the management of organizational processes, technologies, and people which collectively create, acquire, integrate, organize, process, store, disseminate, access, and dispose of the information. Information management is a vast, multi-disciplinary domain that syndicates various subdomains and perfectly intermingles with other domains. This study aims to provide a comprehensive overview of the information management domain from 1970 to 2019. Drawing upon the methodology from statistical text analysis research, this study summarizes the evolution of knowledge in this domain by examining the publication trends as per authors, institutions, countries, etc. Further, this study proposes a probabilistic generative model based on structural topic modeling to understand and extract the latent themes from the research articles related to information management. Furthermore, this study graphically visualizes the variations in the topic prevalences over the period of 1970 to 2019. The results highlight that the most common themes are data management, knowledge management, environmental management, project management, service management, and mobile and web management. The findings also identify themes such as knowledge management, environmental management, project management, and social communication as academic hotspots for future research.

Limited research has been conducted with regards to the applicability of topic extraction techniques in Sociology. Addressing the modern methodological opportunities, and responding to the skepticism with regards to the absence of theoretical foundations supporting the use of text analytics, I argue that Latent Semantic Analysis (LSA), complemented by other text analysis techniques and multivariate techniques, can constitute a unique hybrid method that can facilitate the sociological interpretations of web-based textual data. To illustrate the applicability of the hybrid technique, I developed two case studies. My first case study is associated with the Sociology of media. It focuses on the topic extraction and sentiment polarization among partisan texts posted on two major news sites. I find evidence of highly polarized opinions on comments posted on the Huffington Post and the Daily Caller. The highest polarizing topic was associated with a commentator’s reference on Hoodies in the context of the Trayvon Martin’s incident. My findings support contemporary research suggesting that media pundits frequently use tactics of outrage to provoke polarization of public opinion. My second case study contributes to the research domain of the Sociology of knowledge. The hybrid method revealed evidence of topical divides and topical “bridges” in the intellectual landscape of the British and the American sociological journals. My findings confirm the theoretical assertions describing Sociology as a fractured field, and partially support the existence of more globalized topics in the discipline.