Probabilistic Topic Model Research Articles

Digital Nomads in Conversation: Reddit-based Analysis and the Future of Nomadic versus Migrant Career Journeys

We examine digital nomadism through the lens of the Intelligent Careers framework and compare this emerging career form with more traditional migrant careers. We show how digital nomads navigate their career paths by leveraging online platforms for casual storytelling and knowledge sharing. Our analysis uses probabilistic topic modeling to analyze 66,601 Reddit posts from the DigitalNomad subreddit to uncover insights into digital nomads’ career management strategies. We categorize discussions under the three competencies of the Intelligent Careers framework: knowing-why (motivations and aspirations), knowing-how (skills and adaptability), and knowing-whom (networks and social capital). Most of the conversations concerned practical aspects of nomadic life (knowing-how), differentiating their narrative from the more permanent and often structural hurdles that migrants typically face. Discussions on the knowing-why on the other hand highlight the integration of work and leisure as a significant motivator, while at the same time debating the loss of “home.” The knowing-whom conversations reveal digital nomads’ reliance on online and offline networks for support and work opportunities, showcasing the role of digital platforms in fostering community and collaboration among nomads and revealing strategies for maintaining personal relationships and friendships across boundaries. Digital nomads in some ways resemble migrant actors (e.g., through cost-benefit calculations), but are also significantly different because of temporary nature of their movement and completely portable work lives. We contribute to the broader discourse on contemporary careers and the future of work in the digital era, emphasizing the importance of adaptability, network building, and aligning personal values with career aspirations.

Exploring the built environment impacts on Online Car-hailing waiting time: An empirical study in Beijing

Waiting time is a critical metric for evaluating the quality of online car-hailing services, with studies indicating a close association between Online Car-hailing waiting time (CWT) and the urban built environment (BE). Points of interest (POI) data is widely utilized to characterize the built environment. However, quantifying the proper combination or spatial relationships between different types of POIs to capture the functional features of the built environment within a region poses a challenge. This paper proposes a framework for analyzing built environment characteristics based on a semantic probabilistic topic model utilizing Latent Dirichlet Allocation, demonstrating that integrating the building area of POIs along with the travel activity intensity at their respective locations enables a more precise identification of regional functions. Moreover, while some studies have explored the correlation between the built environment and waiting time, few have evaluated the nonlinear interactions between them. On this foundation, employing the machine learning technique XGBoost model in conjunction with online car-hailing order data, we probe the relationship between the built environment and waiting time. The research indicates that CWT is comprehensively affected by multiple factors. Taking weekday evening peak period as an example, the CWT of Central-urban area, where has dense commercial and office land use, positively correlated with commercial and office topics, while negatively correlated with educational, residential, leisure and healthcare topics, leading to a longer CWT in the Central-urban. In addition, the interaction between BEs can weakens their individual effects on CWT. A higher degree of job-residence balance can mitigate the negative impact of residential and office topics on increasing CWT, particularly in Intermediate-urban areas. Additionally, BE topics may also suppress the positive effect of road density on reducing CWT. The relationship between the BE and CWT exhibits threshold effects and a V-shaped relationship, indicating that the BE is significantly associated with CWT only within specific ranges. This correlation also exhibits a gradient pattern from the Central-urban to the Sub-urban, especially concerning office and residential topics. These outcomes elucidate the salient ranges of the built environment that exert substantial impacts on waiting time, informing strategic planning for online car-hailing dispatch and urban development to augment passenger travel satisfaction.

Unveiling mobility patterns beyond home/work activities: A topic modeling approach using transit smart card and land-use data

In this paper, a probabilistic topic modeling algorithm called Latent Dirichlet Allocation (LDA) is implemented to infer trip purposes from activity attributes revealed from smart card transit data in an unsupervised manner. While most literature focused on finding patterns for home and work activities, we further investigated non-home and non-work-related activities to detect patterns associated with them. Temporal attributes of activities are extracted from trip information recorded by Tehran subway’s automatic fare collection system. In addition, land-use data is also incorporated to further enhance spatial attributes for non-home/work activities. Various activity attributes such as start time, duration, and frequency in addition to land-use data are used to infer the activity purposes and patterns. We identified 14 different patterns related to non-commuting activities on the basis of both their temporal and spatial attributes including educational, recreational, commercial, and health and other service-related activity types. We investigated passengers’ activity pattern and behavior changes before and during COVID-19 pandemic by comparing the discovered patterns. For recreational patterns it is revealed that not only has the number of recreational patterns dropped, but also the duration of recreational activities decreased. Morning patterns of educational activities have also been eliminated and number of commercial activities was decreased during COVID-19. The proposed model demonstrates the ability to capture travel behavior changes for different disruptions using smart card transit data without performing costly and time consuming manual surveys which can be useful for authorties and decision makers.

A topic model analysis of students' gendered expectations using surveyed critiques of lecturers

Student evaluations of teaching (SET) have been examined over the years to better understand the student experience, with an increasing portion of literature exploring the presence of implicit bias in SET surveys against minority gender and ethnic lecturers. This study explores free-text comments made by students from a large public university in Australia over the period 2010–2016, using a semi-supervised statistical approach. Data were collected via surveys administered online at the end of each course to every student officially enrolled in that course via the learning management system, and completion of the surveys was voluntary. We build a probabilistic topic model which incorporates student and lecturer characteristics into the topic formation process. We make statistical inference on the effects of gender and cultural or language backgrounds based on the topic and word prevalence probabilities. The results showed clear separation of topics discussed between male and female lecturers. From a gendered perspective, our topic analyses have found that students are significantly more likely to critique female lecturers to improve on structural aspects of the course, as well as aspects of time management and control of the lecturing environment. In comparison, male lecturers were significantly more likely to be critiqued on specific aspects related to lecture delivery. Lecturers from non-English speaking backgrounds were more likely to be both critiqued and praised for the clarity of their delivery.

Predicting metro incident duration using structured data and unstructured text logs

Predicting metro incident duration is crucial for passengers and transit operators to choose appropriate response strategies. Most existing research focuses on structured data, the rich information embedded within unstructured incident logs is often neglected. This paper incorporates a probabilistic topic model tailored for short texts, the biterm topic model, into the generic incident duration prediction models. By capturing text co-occurrence patterns through Bayesian inference, the biterm topic model extracts hidden topics from incident narratives, and each topic serves as a condensed summary of detailed incident causes and countermeasures. These extracted topics are then combined with structured information to serve as predictors. We validated our model using five years of incident data from the Hong Kong Mass Transit Railway across two scenarios: with all incident information available and information revealed over time . The results demonstrate that our method significantly improves the prediction accuracy, particularly for incidents lasting longer than 30-minute.

The psycho-social impact of video games on K12 Spanish students

This research aims to study the psycho-social impact of video games on K12 students. For this, a probabilistic topic modelling analysis method based on text mining approach has been performed. This process is based on nodes’ connectivity and it has been developed through K Means approach; by launching the Jenks-Breaks algorithm. The sex differences are calculated according to a nonlinear dynamics approach based on Hurst exponent and multifractal function and the influence of time with the application of the Sobel test. The results show which are the most used video games by K12 and their psycho-social impact on students based on four categories: (1) boredom and sadness, (2) happiness and socialization, (3) immersion, and (4) families' conflicts. There are significant differences between boys and girls depending on the games they use, a factor that increases when playing more than two hours a day. For boys, games like FIFA and Fortnite produce higher levels of immersion and family conflict, while for girls, games are perceived as sources of greater happiness and a means to reinforce friendship and camaraderie, particularly with games like Brawl Stars, Rocket League, and PKXD.

Do transformer missions redirect values of mission-oriented projects? The case of the EU mission ‘Restore our Ocean and Waters’

Transformative mission-oriented innovation policy aims to redirect innovation, but evidence of this directional ability is limited. This paper examines whether transformer missions redirect values reflected by mission-oriented projects. We study the EU Mission ‘Restore our Ocean and Waters’ and use probabilistic topic modelling and thematic analyses to identify, conceptualize, and compare latent values described in 17 policy documents (i.e., strategic layer), 37 mission-oriented projects, and 809 mission-relevant projects (i.e., operational layer). We map how these values changed during the mission launch. The results of this study are ambivalent. On the one hand, the mission launch corresponds with an increase of funded projects of which mission-oriented projects commonly frame efforts towards mission objectives. On the other hand, there is a misalignment between policy and project-level values while the prevalence of project-level values remained largely unaffected by the mission. These mixed results provide a more nuanced understanding of transformer missions’ directional abilities.

Dynamic topic modelling for exploring the scientific literature on coronavirus: an unsupervised labelling technique

AbstractThe work presented in this article focusses on improving the interpretability of probabilistic topic models created from a large collection of scientific documents that evolve over time. Several time-dependent approaches based on topic models were compared to analyse the annual evolution of latent concepts in the CORD-19 corpus: Dynamic Topic Model, Dynamic Embedded Topic Model, and BERTopic. Then COVID-19 period (December 2019–present) has been analysed in greater depth, month by month, to explore the evolution of what is written about the disease. The evaluations suggest that the Dynamic Topic Model is the best choice to analyse the CORD-19 corpus. A novel topic labelling strategy is proposed for dynamic topic models to analyse the evolution of latent concepts. It incorporates content changes in both the annual evolution of the corpus and the monthly evolution of the COVID-19 disease. The generated labels are manually validated using two approaches: through the most relevant documents on the topic and through the documents that share the most semantically similar label topics. The labelling enables the interpretation of topics. The novel method for dynamic topic labelling fits the content of each topic and supports the semantics of the topics.

A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection.

Probabilistic topic modelling has become essential in many types of single-cell data analysis. Based on probabilistic topic assignments in each cell, we identify the latent representation of cellular states. A dictionary matrix, consisting of topic-specific gene frequency vectors, provides interpretable bases to be compared with known cell type-specific marker genes and other pathway annotations. However, fitting a topic model on a large number of cells would require heavy computational resources-specialized computing units, computing time and memory. Here, we present a scalable approximation method customized for single-cell RNA-seq data analysis, termed ASAP, short for Annotating a Single-cell data matrix by Approximate Pseudobulk estimation. Our approach is more accurate than existing methods but requires orders of magnitude less computing time, leaving much lower memory consumption. We also show that our approach is widely applicable for atlas-scale data analysis; our method seamlessly integrates single-cell and bulk data in joint analysis, not requiring additional preprocessing or feature selection steps.

Investigating topic modeling techniques through evaluation of topics discovered in short texts data across diverse domains

The online channel has affected many facets of an individual's identity, commercial, social policy, and culture, among others. It implies that discovering the topics on which these brief writings are focused, as well as examining the qualities of these short texts is critical. Another key issue that has been identified is the evaluation of newly discovered topics in terms of topic quality, which includes topic separation and coherence. A topic modeling method has been shown to be an outstanding aid in the linguistic interpretation of quite tiny texts. Based on the underlying strategy, topic models are divided into two categories: probabilistic methods and non-probabilistic methods. In this research, short texts are analyzed using topic models, including latent Dirichlet allocation (LDA) for probabilistic topic modeling and non-negative matrix factorization (NMF) for non-probabilistic topic modeling. A novel approach for topic evaluation is used, such as clustering methods and silhouette analysis on both models, to investigate performance in terms of quality. The experiment results indicate that the proposed evaluation method outperforms on both LDA and NMF.

A market driven identification of current and emerging skills requiring an MBA degree: a topic modelling approach

ABSTRACT The emerging field of Labour Market Intelligence focuses on extracting real-time information for professional skills. Online job posting websites provide unique opportunities to gather such intelligence, useful for job seekers and academic administrators in charge of preparing the future labour force. We propose a probabilistic topic modelling approach, incorporating occupational ontologies, to extract skillsets required for jobs needing a Master of Business Administration (MBA) degree. Analysing LinkedIn job postings in Pennsylvania, USA, we identified 28 job functions and associated skillsets confirming known demands and revealing new ones not yet identified in government and private labour market reports. By aggregating these skills, we can align MBA curricula with market needs, offering a superior alternative to conventional surveys. Our results are comparable with other popular neural models. Our approach provides improved computational efficiency with simpler methods Additionally, this methodology can be applied globally in other fields with existing occupational ontologies.

Dissection and integration of bursty transcriptional dynamics for complex systems

RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-sequencing data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed an approach, TopicVelo, that disentangles simultaneous, yet distinct, dynamics by using a probabilistic topic model, a highly interpretable form of latent space factorization, to infer cells and genes associated with individual processes, thereby capturing cellular pluripotency or multifaceted functionality. Focusing on process-associated cells and genes enables accurate estimation of process-specific velocities via a master equation for a transcriptional burst model accounting for intrinsic stochasticity. The method obtains a global transition matrix by leveraging cell topic weights to integrate process-specific signals. In challenging systems, this method accurately recovers complex transitions and terminal states, while our use of first-passage time analysis provides insights into transient transitions. These results expand the limits of RNA velocity, empowering future studies of cell fate and functional responses.

What are developers talking about information security? A large-scale study using semantic analysis of Q&A posts.

Digitalization and rapid technological improvement in the present day bring numerous benefits, but they also raise the complexity and diversity of cyber security risks, putting critical information security issues on the agenda. Growing issues and worries about information security endanger not only the security of individuals and organizations but also global social and economic stability. This study investigates the issues and challenges regarding information security by analyzing all the postings on ISSE (Information Security Stack Exchange), a Q&A website focused on information security. In order to identify the primary topics addressed in postings shared on the ISSE platform, we employed a probabilistic topic modeling method called latent Dirichlet allocation (LDA), which is generative in nature and relies on unsupervised machine learning processes. Through this investigation, a total of 38 topics were identified, demonstrating the present state of information security issues and challenges. Considering these topics, a comprehensive taxonomy of seven categories was devised to address information security issues, taking into account their backgrounds and perspectives. Subsequently, we conducted an examination of the prevalence and complexity of the matters at hand. In addition, we have defined the prevailing technologies utilized in the realm of information security, including tasks, certifications, standards, methods, tools, threats, and defenses. We have provided a number of implications for different stakeholders, including academics, developers, educators, and practitioners, who are working towards advancing the field of information security.

Supervised probabilistic latent semantic analysis with applications to controversy analysis of legislative bills

Probabilistic Latent Semantic Analysis (PLSA) is a fundamental text analysis technique that models each word in a document as a sample from a mixture of topics. PLSA is the precursor of probabilistic topic models including Latent Dirichlet Allocation (LDA). PLSA, LDA and their numerous extensions have been successfully applied to many text mining and retrieval tasks. One important extension of LDA is supervised LDA (sLDA), which distinguishes itself from most topic models in that it is supervised. However, to the best of our knowledge, no prior work extends PLSA in a similar manner sLDA extends LDA by jointly modeling the contents and the responses of documents. In this paper, we propose supervised PLSA (sPLSA) which can efficiently infer latent topics and their factorized response values from the contents and the responses of documents. The major challenge lies in estimating a document’s topic distribution which is a constrained probability that is dictated by both the content and the response of the document. To tackle this challenge, we introduce an auxiliary variable to transform the constrained optimization problem to an unconstrained optimization problem. This allows us to derive an efficient Expectation and Maximization (EM) algorithm for parameter estimation. Compared to sLDA, sPLSA converges much faster and requires less hyperparameter tuning, while performing similarly on topic modeling and better in response factorization. This makes sPLSA an appealing choice for latent response analysis such as ranking latent topics by their factorized response values. We apply the proposed sPLSA model to analyze the controversy of bills from the United States Congress. We demonstrate the effectiveness of our model by identifying contentious legislative issues.

Latent Dirichlet Allocation (LDA) topic models for Space Syntax studies on spatial experience

Spatial experience has been extensively researched in various fields, with Space Syntax being one of the most widely used methodologies. Multiple Space Syntax techniques have been developed and used to quantitively examine the relationship between spatial configuration and human experience. However, due to the heterogeneity of syntactic measures and experiential issues in the built environment, a systematic review of socio-spatial topics has yet to be developed for Space Syntax research. In response to this knowledge gap, this article employs an ‘intelligent’ method to classify and systematically review topics in Space Syntax studies on spatial experience. Specifically, after identifying 66 articles using the ‘Preferred Reporting Items for Systematic reviews and Meta-Analyses’ (PRISMA) framework, this research develops generative probabilistic topic models to classify the articles using the Latent Dirichlet Allocation (LDA) method. As a result, this research automatically generates three architectural topics from the collected literature data (A1. Wayfinding behaviour, A2. Interactive accessibility, and A3. Healthcare design) and three urban topics (U1. Pedestrian movement, U2. Park accessibility, and U3. Cognitive city). Thereafter it qualitatively examines the implications of the data and its LDA classification. This article concludes with an examination of the limitations of both the methods and the results. Along with demonstrating a methodological innovation (combining PRISMA with LDA), this research identifies critical socio-spatial concepts and examines the complexity of Space Syntax applications. In this way, this research contributes to future Space Syntax research that empirically investigates the relationships between syntactic and experiential variables in architectural and urban spaces. The findings support a detailed discussion about research gaps in the literature and future research directions.

The Strategy of Discriminating False Comments on the Internet by Fusing Probabilistic Topic and Word Vector Models

With the acceleration of the social process of "Internet+", e-commerce has entered an era of rapid development. In response to the subjective judgments in current false information detection methods and the problem of low classification accuracy caused by the failure to extract semantic information hidden in text, this study proposes Latent Dirichlet Allocation -Word vector-Edge Convolutional Neural Network (LW-ECNN) Model. First, the probabilistic topic model is organically combined with the word vector model, and then the edge path structure is proposed to optimize the Convolutional Neural Network (CNN) discriminative classification model. Finally, the data-side optimization module is combined with the edge CNN classifier to form a web-based false comment discrimination model based on the probabilistic topic and word vector models. The results show that the probabilistic topic and word vector-based online false comment discrimination model has good results in detecting false comments, and can provide data reference for false comment detection by false detection-related departments, which is of great practical significance to assist the network environment cleaning work.

The many ways to Transparency

This article explores a sample of the literature on transparency in the 1984-2020 period through a systematic review. The sample consists of 242 works (articles, books, and book chapters) collected from different academic databases. Latent dirichlet allocation (LDA) probabilistic topic modelling – an unsupervised machine learning approach – is employed in order to classify and construct a typology of topics within the literature. This approach is complemented by a structured overview of the varieties of transparency framework and is aimed at addressing three research questions: a) What analytical approaches are identified in the literature? b) How is transparency conceptualised through such analytical approaches? And, c) where has transparency’s focus been placed in relation to an event-process framework? The findings show unequal methodological approaches, topics, and issues investigated. These insights and the novel approach utilised outline key challenges and opportunities for future transparency research.

A Unified Formal Framework for Factorial and Probabilistic Topic Modelling

Topic modelling has become a highly popular technique for extracting knowledge from texts. It encompasses various method families, including Factorial methods, Probabilistic methods, and Natural Language Processing methods. This paper introduces a unified conceptual framework for Factorial and Probabilistic methods by identifying shared elements and representing them using a homogeneous notation. The paper presents 12 different methods within this framework, enabling easy comparative analysis to assess the flexibility and how realistic the assumptions of each approach are. This establishes the initial stage of a broader analysis aimed at relating all method families to this common framework, comprehensively understanding their strengths and weaknesses, and establishing general application guidelines. Also, an experimental setup reinforces the convenience of having harmonized notational schema. The paper concludes with a discussion on the presented methods and outlines future research directions.

Patients' Perspectives, Experiences, and Concerns With Perianal Fistulae: Insights From Online Targeted-Disease Forums.

Perianal fistulae can undermine physical, emotional, and social well-being in patients with Crohn's disease and are challenging to manage. Social media offers a rich opportunity to gain an in-depth understanding of the impact of perianal fistulae on patients' daily lives outside of controlled environments. In this study, we conducted social media analytics to examine patients' experiences with perianal fistulae and assessed the impact of perianal fistulae on patients' behavior and overall well-being. We used a mixed-method approach to examine 119 986 publicly available posts collected from 10 Crohn's disease forums in the United States between January 01, 2010 and January 01, 2020. Discussions related to Crohn's perianal fistulae were retrieved. We randomly selected 700 posts and qualitatively analyzed them using an inductive thematic approach. We then applied a latent Dirichlet allocation probabilistic topic model to explore themes in an unsupervised manner on the collection of 119 986 posts. In the qualitative analysis, 5 major themes were identified: (1) burden of perianal fistula; (2) challenges associated with treatment; (3) online information seeking and sharing; (4) patient experiences with treatments; and (5) patients' apprehension about treatments. In the quantitative analysis, the percentages of posts related to the major themes were (1) 20%, (2) 29%, (3) 66%, and (4) 28%, while the topic model did not identify theme 5. Social media reveals a dynamic range of themes governing patients' perspectives and experiences with Crohn's perianal fistulae. In addition to the biopsychosocial burden, patients frequently express dissatisfaction with current treatments and often struggle to navigate among available management options.

World Futures Day 2022: A mixed method approach to identify topics of a global futures agenda

World Futures Day (WFD) 2022 was the ninth edition of the participatory futures method organized in the form of an open 24-hr round-the-world global conversation about possible futures and developing an online open large-scale global participatory discussion among self-selected future oriented people from around the world and the public. The paper aims at understanding what are the main future-oriented suggestions and solutions that emerged during WFD 2022 by applying a mixed method approach to the available data. The original qualitative content analysis is improved with automated techniques of text mining analysis (i.e. probabilistic topic models). Geographic Information Systems (GIS) tools to integrate the geographical dimension with the textual data are also experimentally exploited. The output of the analysis, called WFD 2022 Global Futures Agenda, helps the reader cope with the complexity of the topics and of the 24-hour event for further discussion. The analysis shows that the WFD discussion was composed of 4 main clusters and 23 related topics and contents for each: Complex solutions for complex problems, A hyper-technological humanity, Education and learning for a better future, Improving global foresight. In conclusion, further improvements of the methods and of the analysis are discussed.