<p>The methodology leverages medical notes already annotated according to the General Assessment of Functioning (GAF) scale to develop a disease severity PDM for schizophrenia, bipolar type I or mixed bipolar and non-psychotic patients.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Psychiatric patients require continuous monitoring on par with their severity status. Unfortunately, current assessment instruments are often time-consuming. The present thesis introduces several passive digital markers (PDMs) that can help reduce this burden by automating the assessment using medical notes. The methodology leverages medical notes already annotated according to the General Assessment of Functioning (GAF) scale to develop a disease severity PDM for schizophrenia, bipolar type I or mixed bipolar and non-psychotic patients. Topic words that are representative of three disease severity levels (severe impairment, serious impairment, moderate to no impairment) are identified and the top 50 words from each severity level are used to summarize the raw text of the medical notes. The summary of the text is processed by a classifier that generates a disease severity level. Two classifiers are considered: BERT PDM and Clinical BERT PDM. The evaluation of these classifiers showed that the BERT PDM delivered the best performance. The PDMs developed using the BERT PDM can assign medical notes from each encounter to a severe impairment level with a positive predictive value higher than 0.84. These PDMs are generalizable and their development was facilitated by the availability of a substantial number of medical notes from multiple institutions that were annotated by several health care providers. The methodology introduced in the present thesis can support the automated monitoring of the progression of the disease severity for psychiatric patients by digitally processing the medical note produced at each encounter without additional burden on the health care system. Applying the same methodology to other diseases is possible subject to availability of the necessary data.

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A controversial bill aimed to stimulate investment and boost the economy in Indonesia, called the Omnibus Law Bill, is followed by both protest and support expressed in social media prior to its signatories in October 2020. During that time, the Twittersphere is packed with both the Opposition and Support movement of the bill, who both benefit from the use of hashtags. To distinguish an organic grass-roots movement from a propaganda that fits the agenda of the government and elite, a comparison study is conducted with a framework of top-down and bottom-up- mechanism of information virality (Nahon &amp; Hemsley, 2013). The top-down mechanism combined with participatory propaganda theory is designated to explain the Support movement. Vice versa the bottom-up mechanism is combined with connective action theory designed to explain the Opposition movement as its character in line with a contemporary and digital protest movement (Bennett &amp; Segerberg, 2012). As existing research only often studies both networks alone, this unique case provides an opportunity to compare both networks. A mixed-method of Social Network Analysis (SNA) and Topic Modelling used to differentiate the characteristics of both groups, based on both network structure and topics discussed. The finding in regards to the SNA is corresponding to the theoretical framework and previous studies. The loosely organized nature of connective action is reflected in several characteristics of the Opposition Network, in contrast to the element of coordination found in the Support Network. Findings from bi-term topic modeling, however, both contradict and support the hypothesis that suggests more variations in the topics within the Opposition Network as a result of the self-motivated participant and personalized messages (Leong et al., 2019).

Recently, distance education has become popular and has gotten much attention. Information and Communication Technology advances fostered distance learning creation and enabled individuals to participate in the education process via various web-based platforms and study entirely online. Thus, the notion of e-learning and distance learning emerged. Massive Open Online Courses (MOOCs) appeared as part of e-learning in 2008 and attracted great interest, especially during the COVID-19 pandemic. It was anticipated that this kind of study also could be integrated into higher education and revolutionize the learning approach. However, several issues related to MOOCs limit their full potential. One of the most significant problems is substantial rate of learners’ attrition. It was discovered that only 5-10 percent of MOOC learners complete a course. This thesis aims to examine what influences individuals’ decision to leave MOOCs and how learners perceive various course components to get ideas regarding how MOOCs could be enhanced. To do this, the mixed-method study was undertaken where quantitative data analysis of learners’ reviews from discussion forums and qualitative interviews were adopted. It allowed to get two perspectives and broaden the thesis out- come. For the current research, data was collected from six courses in three different subjects-«Health», «Art and Humanity/Design» and «Computer/Data Science». In the first part of the work, sentiment analysis and topic modeling using Python packages were carried out, and then the results were used to construct an interview questionnaire. Lexicon-based sentiment analysis technique and LDA topic modeling algorithm were utilized and proved to be robust methods to extract texts’ polarity and peoples’ opinions. In the qualitative part, 19 topics of discussion were identified, which were consolidated into eight topics with higher abstraction – materials, instructor, content, time, assignment, feedback, program(course), and algorithms. Then during the qualitative part, participants expressed their opinions regarding these topics, and analysis codes were predefined, and new topics did not emerge. The results showed learners’ perceptions related to presented topics and how these aspects influence experience with MOOCs. The outcome also showed a slight disparity between different subject learners, in both qualitative and quantitative studies identified topics of discussion were not exactly the same, showing that learners from different educational domains tend to discuss different themes.

Artificial intelligence (AI) and robotics are revolutionizing the traditional paradigm of business operations and transforming consumers’ experiences by promoting human–robot interaction in tourism and hospitality. Nonetheless, research related to customers’ experiences with robot-related services in this industry remains scant. This study thus seeks to investigate hotel customers’ experiences with service robots and how robot-based experiences shape customers’ satisfaction with hotel stays. Specifically, three research questions are addressed: (a) What are hotel customers’ primary concerns about robots and robot-related services? (b) Do hotel customers’ experiences with robotic services shape guests’ overall satisfaction? (c) How do service robots’ attributes affect guests’ forgiveness of robots’ service failure? This dissertation consists of three chapters. Chapter 1 introduces the overall research background. Chapter 2 answers the first two research questions by combining text mining and regression analyses; Chapter 3 addresses the third question by introducing social cognition into this investigation and performing an experiment. Overall, sentiment analyses uncovered customers’ generally positive experiences with robot services. Machine learning via latent Dirichlet allocation modeling revealed three key topics underlying hotel guests’ robot-related reviews—robots’ room delivery services, entertainment and catering services, and front office services. Regression analyses demonstrated that hotel robots’ attributes (e.g., mechanical vs. AI-assistant robots) and robot reviews’ characteristics (e.g., sentiment scores) can influence customers’ overall satisfaction with hotels. Finally, the experimental study verified uncanny valley theory and the existence of social cognition related to service robots (i.e., warmth and competence) by pointing out the interactive effects of robots’ anthropomorphism in terms of their facial expressions, voices, and physical appearance. These findings collectively yield a set of theoretical implications for researchers along with practical implications for hotels and robot developers. / Tourism and Sport

Labeling is the process of giving a set of data a descriptive name. This thesis dealt with documents with no additional information and aimed at clustering them using topic modeling and labeling them using Wikipedia as a second source. Labeling documents is a new field with many potential solutions. This thesis examined one method in a practical setting. Unstructured data was preprocessed and clustered using a topic model. Frequent words from each cluster were used to generate a search query sent to Wikipedia, where titles and categories from the most relevant pages were stored as candidate labels. Each candidate label was evaluated based on the frequency of common cluster words among the candidate labels. The frequency was weighted proportional to the relevance of the original Wikipedia article. The relevance was based on the order of appearance in the search results. The five labels with the highest scores were chosen to describe the cluster. The clustered documents consisted of exam questions that students use to practice before a course exam. Each question in the cluster was scored by someone experienced in the relevant topic by evaluating if one of the five labels correctly described the content. The method proved unreliable, with only one course receiving labels considered descriptive for most of its questions. A significant problem was the closely related data with all documents belonging to one overarching category instead of a dataset containing independent topics. However, for one dataset, 80 % of the documents received a descriptive label, indicating that labeling using secondary sources has potential, but needs to be investigated further. / Märkning handlar om att ge okända data en beskrivning. I denna uppsats behandlas data i form av dokument som utan ytterligare information klustras med temamodellering samt märks med hjälp av Wikipedia som en sekundär källa. Märkning av dokument är ett nytt forskningsområde med flera tänkbara vägar framåt. I denna uppsats undersöks en möjlig metod i en praktisk miljö. Dokumenten förbehandlas och grupperas i kluster med hjälp av en temamodell. Vanliga ord från varje kluster används sedan för att generera en sökfråga som skickas till Wikipedia där titlar och kategorier från de mest relevanta sidorna lagras som kandidater. Varje kandidat utvärderas sedan baserat på frekvensen av kandidatordet bland titlarna i klustret och relevansen av den ursprungliga Wikipedia-artikeln. Relevansen av artiklarna baserades på i vilken ordning de dök upp i sökresultatet. De fem märkningarna med högst poäng valdes ut för att beskriva klustret. De klustrade dokumenten bestod av tentamensfrågor som studenter använder sig av för att träna inför ett prov. Varje fråga i klustret utvärderades av någon med erfarenhet av det i frågan behandlade ämnet. Utvärderingen baserades på om någon av de fem märkningarna ansågs beskriva innehållet. Metoden visade sig vara opålitlig med endast en kurs som erhöll märkningar som ansågs beskrivande för majoriteten av dess frågor. Ett stort problem var att data var nära relaterad med alla dokument tillhörande en övergripande kategori i stället för oberoende ämnen. För en datamängd fick dock 80 % av dokumenten en beskrivande etikett. Detta visar att märkning med hjälp av sekundära källor har potential, men behöver undersökas ytterligare.

The lack of success of the gun control movement and the success of the gun rights movement in the United States have prompted research into the root causes. Although the political infrastructure, organizational resources, and public interest prove to be important factors in a social movement's success, how each social movement frames their arguments is extremely important for proposing policy initiatives and garnering support. In order to understand how gun control and gun rights organizations frame their arguments this study does two things: (1) performs topic modeling on the six gun control organizations' and three gun rights organizations' press statements to see the frames that each social movement engages in, and (2) identifying these frames in the most popular gun control and gun rights organizations on Facebook to predict likes, comments, and shares. This study is able to identify the top frames in the gun control and gun rights social movements and see how followers of each of these movements engage with each of these frames on Facebook.

In my dissertation, I examine varied types of knowledge and how they contribute to innovation generation and selection at both the firm and the industry level using the emerging industry context of small satellites. My research is divided into three papers. In Paper One, I take a supply-demand perspective and examine how suppliers of technology—with their unique knowledge of science and technology—and users of technology—with their unique knowledge of demand—contribute to innovation generation and selection over the industry lifecycle. Results show that the contributions of suppliers and users vary based on unique aspects of innovation, such as novelty, breadth, and coherence – and also over the industry life cycle. In Paper Two, I study how firms overcome science-business tension in their pursuit of novel innovation. I examine unique aspects of knowledge: scientists' business knowledge and CEOs' scientific knowledge. I show that CEOs' scientific knowledge is an important driver of firms' novel pursuits and that this impact is higher when scientists do not have business knowledge. In the third paper, I further examine how scientists with high technological and scientific knowledge—i.e., star scientists—impact firm innovation generation and selection. With a focus on explorative and exploitative innovation, I develop theory on the boundary conditions of stars' impact on firm level outcomes. I propose that individual level contingencies—i.e., stage of employment—and organizational level contingencies—explorative or exploitative innovation—both facilitate and hinder stars' impact on firms' innovative pursuits. / Doctor of Philosophy / In my dissertation, I study innovation at both the firm level and the industry level using the emerging industry context of small satellites. My dissertation divides into three papers. In Paper One, I study unique aspects of innovation at the industry level taking a supply-demand perspective. Since novelty, breadth, and convergence of innovation are all important drivers of the emergence and evolution of industries, I examine how supply side or demand side actors contribute to unique aspects of innovation over the industry life cycle. Results suggest that both suppliers and users of technology make important contributions to innovation, however, their respective contributions vary to novelty, breadth, and convergence of innovation. This impact varies over the industry life cycle. In Paper Two, I study how firms pursue novel innovation as main creator of economic value for firms. Firms need both scientific and technological knowledge in their pursuit of novel innovation. However, firms often struggle to overcome science-business tensions. Focusing on CEOs and scientists as two main drivers of innovation, I study how CEOs’ scientific knowledge and scientists’ business knowledge help firms overcome business-science tension. Results suggest that the likelihood of firms’ novel pursuit is higher when CEOs have scientific knowledge and scientists do not have business knowledge. In Paper Three, I further examine how high-performing scientists—i.e., star scientists—impact explorative and exploitative innovation. I propose that the stage of employment of individuals and goal context of firms are important contingencies that impact how stars impact firm level innovation.