This dissertation describes a research project concerned with establishing the topical structure of long informal documents. In this research, we place special emphasis on literary data, but also work with speech transcripts and several other types of data. It has long been acknowledged that discourse is more than a sequence of sentences but, for the purposes of many Natural Language Processing tasks, it is often modelled exactly in that way. In this dissertation, we propose a practical approach to modelling discourse structure, with an emphasis on it being computationally feasible and easily applicable. Instead of following one of the many linguistic theories of discourse structure, we attempt to model the structure of a document as a tree of topical segments. Each segment encapsulates a span that concentrates on a particular topic at a certain level of granularity. Each span can be further sub-segmented based on finer fluctuations of topic. The lowest (most refined) level of segmentation is individual paragraphs. In our model, each topical segment is described by a segment centre -- a sentence or a paragraph that best captures the contents of the segment. In this manner, the segmenter effectively builds an extractive hierarchical outline of the document. In order to achieve these goals, we use the framework of factor graphs and modify a recent clustering algorithm, Affinity Propagation, to perform hierarchical segmentation instead of clustering. While it is far from being a solved problem, topical text segmentation is not uncharted territory. The methods developed so far, however, perform least well where they are most needed: on documents that lack rigid formal structure, such as speech transcripts, personal correspondence or literature. The model described in this dissertation is geared towards dealing with just such types of documents. In order to study how people create similar models of literary data, we built two corpora of topical segmentations, one flat and one hierarchical. Each document in these corpora is annotated for topical structure by 3-6 people. The corpora, the model of hierarchical segmentation and software for segmentation are the main contributions of this work.

In this thesis focus is on developing an online social network application with a Peer-to-Peer infrastructure motivated by BestPeer++ architecture and BATON overlay structure. BestPeer++ is a data processing platform which enables data sharing between enterprise systems. BATON is an open-sourced project which implements a peer-to-peer with a topology of a balanced tree. We designed and developed the components for users to manage their accounts, maintain friend relationships, and publish their contents with privacy control and newsfeed, notification requests in this social network- ing application. We also developed a Hashtag Recommendation system for this social net- working application. A user may invoke a recommendation procedure while writing a content. After being invoked, the recommendation pro- cedure returns a list of candidate hashtags, and the user may select one hashtag from the list and embed it into the content. The proposed ap- proach uses Latent Dirichlet Allocation (LDA) topic model to derive the latent or hidden topics of different content. LDA topic model is a well developed data mining algorithm and generally effective in analyzing text documents with different lengths. The topic model is further used to identify the candidate hashtags that are associated with the texts in the published content through their association with the derived hidden top- ics. We considered different methods of recommendation approach for the pro- cedure to select candidate hashtags from different content. Some methods consider the hashtags contained in the contents of the whole social net- work or of the user self. These are content-based recommendation tech- niques which matching user’s own profile with the profiles of items.. Some methods consider the hashtags contained in contents of the friends or of the similar users. These are collaborative filtering based recommendation techniques which considers the profiles of other users in the system. At the end of the recommendation procedure, the candidate hashtags are or- dered by their probabilities of appearance in the content and returned to the user. We also conducted experiments to evaluate the effectiveness of the hashtag recommendation approach. These experiments were fed with the tweets published in Twitter. The hit-rate of recommendation is measured in these experiments. Hit-rate is the percentage of the selected or relevant hashtags contained in candidate hashtags. Our experiment results show that the hit-rate above 50% is observed when we use a method of recommendation approach independently. Also, for the case that both similar user and user preferences are considered at the same time, the hit-rate improved to 87% and 92% for top-5 and top-10 candidate recommendations respectively.

Myocardial Infarction: A Study of the Effects On Patient Compliance of Structured Education and Participation of the Significant Other This study was designed to explore the effects of the independent variables of patient education and the significant other on compliance. The purpose of the study was to test three hypotheses predicting that subjects who receive structured education with their significant other would have higher compliance rates with health care recommendations than would subjects who receive structured and unstructured education without their significant other. The study was conducted with a convenience sample of 12 male patients who had a significant other and had not experienced a previous myocardial infarction within five years. The convenience sample was then randomly and equally allocated into three groups. The control group received unstructured education as currently practiced by nursing staff. One experimental group received structured education from the investigator and the other experimental group of subjects and their significant other received structured education from the investigator. Using a semi-structured interview guide, the investigator interviewed each subject at one month and at three to four months postdischarge from hospital to determine compliance rates with physical activity, dietary, and medication health care recommendations as prescribed by the subject's physician. Open-ended questions were used to determine recommendations and difficulties encountered by noncompliers. More specific questions were used to allow subjects to rate their compliance. Results were subjected to the Kruskal-Wallis rank-sum test with one-way analysis of variance. Statistically significant differences (p < .05) were not found suggesting that method of patient education was not a valid prediction of compliant behaviour. The insignificant findings of this study need to be interpreted with caution because of the small sample size and between group differences of the demographic variables of age and employment. From general observations of the total sample, personal definitions of health, simultaneous demands and the extent of behavioural changes required, and the demographic variables of education and employment appear to influence compliance. These findings suggest that individual differences have an impact on compliant behaviour. Findings also suggest that the significant others of patients with myocardial infarctions are actively involved with the therapeutic regimen prescribed for their mates. The study discusses implications and recommendations for nurse practitioners and researchers who wish to improve their care of myocardial infarction patients and their significant others. / Applied Science, Faculty of / Nursing, School of / Graduate

This experimental study was designed to determine the effectiveness of a structured preoperative teaching programme for the adult surgical patient as measured by several indicators. The major questions asked in this study were: What are the effects of a structured preoperative teaching programme upon the adult surgical patient's length of hospital stay, postoperative complications, number of analgesics administered postoperatively, recall of knowledge explained preoperatively, and satisfaction with his preoperative teaching. This study was conducted over a four month period, on one surgical ward of a large general hospital. A total of forty subjects met the criteria of the study, and their informed consent was obtained. The first twenty subjects were assigned to the control group, and received the unstructured, pre-existing preoperative instruction from the staff nurses. The second twenty subjects made up the experimental group and received structured preoperative teaching in small groups conducted by the investigator, with the aid of a slide-taped programme developed specifically for the study. Prior to discharge, each subject was given two questionnaires to complete, and data were collected by means of a patient profile sheet. The two groups of subjects were found to be similar when compared on selected characteristics. The alternative hypotheses of the study were analyzed by means of a t-test, and chi square test at the .05 level of significance. The results revealed no significant effect of the structured preoperative teaching programme upon the adult surgical patient's length of hospital stay, postoperative complications, number of analgesics administered postoperatively, or the degree of satisfaction attained from the preoperative teaching he received. However, statistical significance was found for the patient's ability to recall knowledge explained preoperatively. Implications of this study and recommendations for future research were also suggested. / Applied Science, Faculty of / Nursing, School of / Graduate

Reading news articles from online sources has become a major choice of obtaining information for many people. Authors who wrote news articles could introduce their own biases either unintentionally or intentionally by using or choosing to use different words to describe otherwise neutral and factual information. Such intentional word choices could create conflicts among different social groups, showing explicit and implicit biases. Any type of biases within the text could affect the reader’s view of the information. One type of biases in natural language is gender bias that had been discovered in a lot of Natural Language Processing (NLP) models, largely attributed to implicit biases in the training text corpora. Analyzing gender bias or stereotypes in such large corpora is a hard task. Previous methods of bias detection were applied to short text like tweets, and to manually built datasets, but little works had been done on long text like news articles in large corpora. Simply detecting bias on annotated text does not help to understand how it was generated and reproduced. Instead, we used structural topic modeling on a large unlabelled corpus of news articles, incorporated qualitative results and quantitative analysis to examine how gender bias was generated and reproduced. This research extends the prior knowledge of bias detection and proposed a method for understanding gender bias in real-world settings. We found that author gender correlated to the topic-gender prevalence and skewed media-gender distribution assist understanding gender bias within news articles.

Shared micromobility defines as the shared use of light and low-speed vehicles such as bike and scooter in which users have short-term access on an as-needed basis. As shared micromobility, as one of the most viable and sustainable modes of transportation, has emerged in the U.S. over the last decade., understanding different aspects of these modes of transportation help decision-makers and stakeholders to have better insights into the problems related to these transportation options. Designing efficient and effective shared micromobility programs improves overall system performance, enhances accessibility, and is essential to increase ridership and benefit commuters. This dissertation aims to address three vital aspects of emerging shared micromobility transportation options with three essays that each contribute to the practice and literature of sustainable transportation. Chapter one of this dissertation investigates public opinion towards dockless bikes sharing using a mix of statistical and natural language processing methods. This study finds the underlying topics and the corresponding polarity in public discussion by analyzing tweets to give better insight into the emerging phenomenon across the U.S. Chapter two of this dissertation proposes a new framework for the micromobility network to improve accessibility and reduce operator costs. The framework focuses on highly centralized clubs (known as k-club) as virtual docking hubs. The study suggests an integer programming model and a heuristic approach as well as a cost-benefit analysis of the proposed model. Chapter three of this dissertation address the risk perception of bicycle and scooter riders’ risky behaviors. This study investigates twenty dangerous maneuvers and their corresponding frequency and severity from U.S. resident’s perspective. The resultant risk matrix and regression model provides a clear picture of the public risk perception associated with these two micromobility options. Overall, the research outcomes will provide decision-makers and stakeholders with scientific information, practical implications, and necessary tools that will enable them to offer better and sustainable micromobility services to their residents.

TOPICAL ANALYSIS OF FAKE NEWS 4 Abstract Since the 2016 US presidential campaign of Donald Trump, the term "fake news" has permeated mainstream discourse. The proliferation of disinformation and false narratives on social media platforms has caused concern in security circles in both the United States and European Union. Combining latent Dirichlet allocation, a machine learning method for text mining, with themes on topical analysis, ideology and social identity drawn from Critical Discourse theory, this thesis examines the elaborate fake news environments of two well-known English language websites: InfoWars and Sputnik News. Through the exploration of the ideologies and social representations at play in the larger thematic structure of these websites, a picture of two very different platforms emerges. One, a white dominant, somewhat isolationist counterculture mindset that promotes a racist and bigoted view of the world. Another, a more subtle world order-making perspective intent on reaching people in the realm of the mundane. Keywords: fake news, Sputnik, InfoWars, topical analysis, latent Dirichlet allocation Od americké prezidentské kampaně Donalda Trumpa z roku 2016, termín "fake news" (doslovně falešné zprávy) pronikl do mainstreamového diskurzu. Šíření dezinformací a falešných zpráv na platformách...

The Inter-Battery Topic Model (IBTM) is an extension of the well known Latent Dirichlet Allocation (LDA) topic model. It gives a factorized representation of multimodal (in this case two views) data, which better separates variation in observed data that is present in both views from variation that is present only in one of the separate views. This thesis is an evaluation and application study of this model with the aim of showing how it can be used in the very difficult classification task of dating grayscale face portraits from a dataset collected from highschool yearbooks. This task has very high intra-class variation and low inter-class variation which calls for techniques to extract the necessary information. An online-trained model is also implemented and evaluated as well as a simplification of the model more suited for this data specifically. The results show improved performance over LDA showing that the factorizing property of IBTM has a positive effect on performance for this type of classification task. / Inter-Battery Topic Model (IBTM) är en vidareutveckling av den välkända Latent Dirichlet Allocation (LDA) topic-modellen. Den ger en faktoriserad representation av multimodal data som bättre separerar variation i datat som finns i båda datavyer från den som finns i de enskilda datavyerna. Det här examensarbetet är en evaluering och applikationsstudie av modellen, med mål att visa hur den kan användas i den mycket svåra klassificeringsuppgiften att datera svartvita bilder från ett dataset skapat från amerikanska highschool-årsboksfoton. Denna klassificeringsuppgift har väldigt hög inom-klass variation samt väldigt låg mellan-klass variation vilket kräver bättre sätt att extrahera den nödvändiga information för bra klassificering. En online-tränad variant av modellen implementeras och evalueras också, samt en modellvariant som är mer anpassad för just denna typ av data. Resultaten visar bättre prestanda än LDA vilket visar att den faktoriserade representationen från IBTM har en positiv effekt på prestanda in en klassificeringsuppgift av den här typen.

Indiana University-Purdue University Indianapolis (IUPUI) / Efficient topic modeling is needed to support applications that aim at identifying main themes from a collection of documents. In this thesis, a reduced vector embedding representation and particle swarm optimization (PSO) are combined to develop a topic modeling strategy that is able to identify representative themes from a large collection of documents. Documents are encoded using a reduced, contextual vector embedding from a general-purpose pre-trained language model (sBERT). A modified PSO algorithm (pPSO) that tracks particle fitness on a dimension-by-dimension basis is then applied to these embeddings to create clusters of related documents. The proposed methodology is demonstrated on three datasets across different domains. The first dataset consists of posts from the online health forum r/Cancer. The second dataset is a collection of NY Times abstracts and is used to compare

A sudden rise in right wing parties has occurred throughout Europe, and this is no exception in Sweden. The Sweden Democrats have gone from a small, extremist party with founders who have roots in nazism and fascism, into the third largest party in Sweden. This study have analyzed articles from 2005, 2006, 2013, 2014, 2018 and 2021 in two of the biggest newspapers in Sweden, Aftonbladet and Expressen in order to discover patterns in media material that showcases how traditional and established media outlets such as the ones mentioned have changed, or not changed, their coverage surrounding The Sweden Democrats. The aim of the study is to analyze the apparent normalization of The Sweden Democrats through frames such as labeling, tonality and topics present in news articles from Aftonbladet and Expressen throughout their rise to power.The research questions were: Quantitative research question: - Has The Sweden Democrats been normalized in Aftonbladet and Expressen from 2005 to 2021 based on labeling, topic and tonality? If so, how? Qualitative research question:- What is the discourse(s) and frames surrounding The Sweden Democrats in Aftonbladet and Expressen in the consecutive years? In order to answer the quantitative research questions, and to fulfill the aim of the study, a content analysis was first done in a large number of articles throughout the years. In order to answer the qualitative research questions, a framing analysis with purposive sampling followed the content analysis, in order to take a closer look into the frames and discourses present in the material throughout the years. The findings of the study showed that there has been a shift in tonality, topics and labeling throughout the years. The findings showcased that the party in the beginning were labeled as extremists, while gradually becoming labeled as neutral and eventually established in the later years. This showcased a normalization of the party in the media throughout their rise in power.