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Changing research topic trends as an effect of publication rankings – The case of German economists and the Handelsblatt RankingBuehling, Kilian 07 September 2023 (has links)
In order to arrive at informed judgments about the quality of research institutions and individual scholars, funding agencies, academic employers and researchers have turned to publication rankings. While such rankings, often based on journal citations, promise a more efficient and transparent funding allocation, individual researchers are at risk of showing adaptive behavior. This paper investigates whether the use of journal rankings in assessing the quality of scholarly research results in the unintended consequence of researchers adapting their research topics to the publishing interests of high-ranked journals. The introduction of the Handelsblatt Ranking (HBR) for economists in German language institutions serves as a quasi-natural experiment, allowing for an examination of research topic dynamics in economics via topic modeling and text classification. It is found that the Handelsblatt Ranking did not cause a significant shift of topics researched by German-affiliated authors in comparison to their international counterparts, even though topic convergence is apparent.
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AUTOMATED ASSESSMENT OF PSYCHIATRIC PATIENTS USING MEDICAL NOTESShuo Wang (14094501) 03 February 2023 (has links)
<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>
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Automated Assessment of Psychiatric Patients Using Medical NotesWang, Shuo 12 1900 (has links)
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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UNDERSTANDING AND IDENTIFYING LARGE-SCALE ADAPTIVE CHANGES FROM VERSION HISTORIESMeqdadi, Omar Mohammed 30 July 2013 (has links)
No description available.
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Non-parametric Clustering and Topic Modeling via Small Variance Asymptotics with Local SearchSingh, Siddharth January 2013 (has links)
No description available.
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The Hashtags Rivalry behind the Controversial Bill : A comparative study on the Opposition and Support Movement of Omnibus Law Bill in Indonesia. / The Hashtags Rivalry behind the Controversial Bill : A comparative study on the Opposition and Support Movement of Omnibus Law Bill in Indonesia.Damayanti, Imelda January 2021 (has links)
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 & 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 & 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).
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Investigating MOOCs with the use of sentiment analysis of learners' feedback. What makes great MOOCs across different domains?Nefedova, Natalia January 2022 (has links)
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.
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Descriptive Labeling of Document Clusters / Deskriptiv märkning av dokumentklusterÖsterberg, Adam January 2022 (has links)
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.
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Gauging Gun-Based Social Movements Frames: Identifying Frames through Topic Modeling and Assessing Public Engagement of Frames through Facebook Media PostsPrasanna, Ram 07 1900 (has links)
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.
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Multiset Model Selection and Averaging, and Interactive StorytellingMaiti, Dipayan 23 August 2012 (has links)
The Multiset Sampler [Leman et al., 2009] has previously been deployed and developed for efficient sampling from complex stochastic processes. We extend the sampler and the surrounding theory to model selection problems. In such problems efficient exploration of the model space becomes a challenge since independent and ad-hoc proposals might not be able to jointly propose multiple parameter sets which correctly explain a new pro- posed model. In order to overcome this we propose a multiset on the model space to en- able efficient exploration of multiple model modes with almost no tuning. The Multiset Model Selection (MSMS) framework is based on independent priors for the parameters and model indicators on variables. We show that posterior model probabilities can be easily obtained from multiset averaged posterior model probabilities in MSMS. We also obtain typical Bayesian model averaged estimates for the parameters from MSMS. We apply our algorithm to linear regression where it allows easy moves between parame- ter modes of different models, and in probit regression where it allows jumps between widely varying model specific covariance structures in the latent space of a hierarchical model.
The Storytelling algorithm [Kumar et al., 2006] constructs stories by discovering and con- necting latent connections between documents in a network. Such automated algorithms often do not agree with user's mental map of the data. Hence systems that incorporate feedback through visual interaction from the user are of immediate importance. We pro- pose a visual analytic framework in which such interactions are naturally incorporated in to the existing Storytelling algorithm through a redefinition of the latent topic space used in the similarity measure of the network. The document network can be explored us- ing the newly learned normalized topic weights for each document. Hence our algorithm augments the limitations of human sensemaking capabilities in large document networks by providing a collaborative framework between the underlying model and the user. Our formulation of the problem is a supervised topic modeling problem where the supervi- sion is based on relationships imposed by the user as a set of inequalities derived from tolerances on edge costs from inverse shortest path problem. We show a probabilistic modeling of the relationships based on auxiliary variables and propose a Gibbs sampling based strategy. We provide detailed results from a simulated data and the Atlantic Storm data set. / Ph. D.
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