Topic Models

Understanding thematic trends and user roles is an important challenge in the field of information retrieval. In this paper, we worked on a novel probabilistic model for capturing the evolution of user's interests in terms of content they generate over time. Our approach ATTention (a name derived from analysis of Authors and Topics in the Temporal context) addresses this problem by using of Bayesian modeling of relations between authors, latent topics and temporal data. The results of our experiments with scientific publications datasets are presented. The results show that using the ATTention model, it is possible to capture the change in users' interests over time. We also discuss opportunities of using the model in certain mining and recommendation scenarios.

Latent Dirichlet Allocation, also known as LDA, is a generative probabilistic model in the domain of Natural Language Processing (NLP), that is frequently used for the purpose of discovering latent thematic structures that are concealed inside large text datasets. The first part of the paper is a Literature Review of LDA Applied in NLP. This review addresses LDA's most important improvements to the NLP field and demonstrates how well it works at finding semantic structures in unstructured text. Next, the paper discusses Parameter Estimation Methods. It covers methods like Variational Inference and Gibbs Sampling that are used to guess how topics are spread out across texts and words. These methods are tested to see how well they work with big datasets and how quickly they can be computed. In the Hyperparameter Tuning section, we explore what happens when we change important hyperparameters like the Dirichlet priors: topic-document distribution and word-topic distribution. We also discuss ways to improve model performance, such as grid search and Bayesian optimization. Finally, the paper addresses the limitations of LDA, such as the assumption of "bag-of-words," the fact that topics can only be roughly understood, the use of "Dirichlet priors," the ability to scale, and the sensitivity to hyperparameters. The purpose of this work is to give researchers and practitioners with a comprehensive understanding of the statistical mechanics of LDA and to guide its ap plication in a variety of domains.

Online trading is essential for day-today life in the rapid growth of internet and e-commerce. Customers are intended through the online advertisements and necessity to take decision making for the product to purchase among similar ones. Collaborative Filtering Techniques are used in Recommendation System to forecasting the rating of product for the customer's right choice. In this paper, Samsung Mobile Review Rating (SMRR) dataset are considered for study. The BFGS algorithm runs for different latent factors with bias values and shows that RMSE converged in fewer iterations and hence forecasting the unknown ratings.

Digitization and computer science have established a whole new set of methods to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, statistical algorithms that automatically infer themes from large collections of texts. In this article, I present an introduction to topic modeling and give a very first review on the research using topic models. I illustrate their capacity by applying them on 2.675 articles published in the Journal of Economic History between 1941 and 2016. This contributes to traditional research on the JEH and to current research on the cliometric revolution.

Online reviews have been gaining relevance in hospitality and tourism management and represent an important research avenue for academia. This study illustrates the discrimination between positive and negative reviews based on single word items and the sector-specific relevance of hidden topics. By probing two parallel approaches of entirely unrelated analytical methods (Penalized Support Vector Machines and Latent Dirichlet Allocation) the analysts explore differences in language between favorable and unfavorable reviews in three service settings (hotels, restaurants, and attractions). The percentage of correctly predicted positive and negative review reports by means of individual word items does not decrease if reports from the three tourism businesses are analyzed together. However, there is limited generalizability of the discriminant words across the three businesses. Also, the latent topics relevant for generating customers' review reports differ significantly between the three sectors of tourism businesses.

This article examines the automatic identification of Web registers, that is, text varieties such as news articles and reviews. Most studies have focused on corpora restricted to include only preselected classes with well-defined characteristics. These corpora feature only a subset of documents found on the unrestricted open Web, for which register identification has been particularly difficult because the range of linguistic variation on the Web is known to be substantial. As part of this study, we present the first open release of the Corpus of Online Registers of English (CORE), which is drawn from the unrestricted open Web and, currently, is the largest collection of manually annotated Web registers. Furthermore, we demonstrate that the CORE registers can be automatically identified with competitive results, with the best performance being an F1-score of 68% with the deep learning model BERT. The best performance was achieved using two modeling strategies. The first one involved...

Despite several decades of research in document analysis, recognition of unconstrained handwritten documents is still considered a challenging task. Previous research in this area has shown that word recognizers perform adequately on constrained handwritten documents which typically use a restricted vocabulary (lexicon). But in the case of unconstrained handwritten documents, state-of-the-art word recognition accuracy is still below the acceptable limits. The objective of this research is to improve word recognition accuracy on unconstrained handwritten documents by applying a post-processing or OCR correction technique to the word recognition output. In this paper, we present two different methods for this purpose. First, we describe a lexicon reduction-based method by topic categorization of handwritten documents which is used to generate smaller topic-specific lexicons for improving the recognition accuracy. Second, we describe a method which uses topic-specific language models and a maximum-entropy based topic categorization model to refine the recognition output. We present the relative merits of each of these methods and report results on the publicly available IAM database.

Online Social Networks (OSNs), such as Twitter, offer attractive means of social interactions and communications, but also raise privacy and security issues. The OSNs provide valuable information to marketing and competitiveness based on users posts and opinions stored inside a huge volume of data from several themes, topics, and subjects. In order to mining the topics discussed on an OSN we present a novel application of Louvain method for TopicModeling based on communities detection in graphs by modularity. The proposed approach succeeded in finding topics in five different datasets composed of textual content from Twitter and Youtube. Another important contribution achieved was about the presence of texts posted by spammers. In this case, a particular behavior observed by graph community architecture (density and degree) allows the indication of a topic strength and the classification of it as natural or artificial. The later created by the spammers on OSNs.

One of those tools, Latent Dirichlet Allocation (LDA) is used in this research to identify the latent topics in the New Testament once it is collated into two corpora: Paul's books; and the other books in the New Testament. The purpose is to investigate a longstanding theological question concerning Paul's authorship of the New Testament Book of Hebrews. The hidden topics discovered by LDA are mapped through posterior analysis to the books in the New Testament to determine their Paulene content. The findings from this analysis do not support Paul's authorship of Hebrews. The latent topics that generated Hebrews are more closely related to the other authors than to Paul. Perplexity analysis is used to determine the optimal number of topics.

We develop and apply unsupervised statistical topic models, in particular Latent Dirichlet Allocation, to identify functional components of source code and study their evolution over multiple project versions. We present results for two large, open source Java projects, Eclipse and Ar-goUML, which are well-known and well-studied within the software mining community. Our results demonstrate the effectiveness of probabilistic topic models in automatically summarizing the temporal dynamics of software concerns, with direct application to project management and program understanding. In addition to detecting the emergence of topics on the release timeline which represent integration points for key source code functionality, our techniques can also be used to pinpoint refactoring events in the underlying software design, as well as to identify general programming concepts whose prevalence is dependent only of the size of the code base to be analyzed. Complete results are available from our supplementary materials website at evolution.html.

The incorporation of artificial intelligence (AI) into educational management offers personalized learning, adaptive tutoring, and efficient resource management. However, ethical considerations such as fairness, transparency, accountability, and privacy are crucial. This paper reviews literature and conducts a bibliometric analysis on ethical AI in educational leadership, analyzing publications from Web of Science and Scopus. The findings highlight key themes and research gaps, emphasizing the need for ethical guidelines and further research to ensure AI benefits equity, access, and student welfare. This study informs stakeholders about the current status of ethical AI in educational leadership, guiding future responsible AI practices.

Zou Fan" is currently the largest "tree hole" on Weibo, where people having suicidal ideation often express their thoughts and use this channel to seek support. Therefore, early suicide monitoring and timely crisis intervention based on artificial intelligence technology are needed for this social media user group. This research was based on the knowledge graph technology, whereby "Tree Hole Intelligent Agent" (i.e., Artificial Intelligence Program) was used to identify "Zou Fan Tree Hole" users at high risk for suicide, and then, the "Tree Hole Action" carried out proactive suicide crisis intervention with them. The "Tree Hole Action" has temporarily prevented 3,629 potential suicides. The "Tree Hole Action" plays a significant role in suicide risk monitoring and crisis intervention for social media users and has been seen to have an important social impact.

Unstructured geo-text annotations volunteered by users of web map services enrich the basic geographic data. However, irrelevant geo-texts can be added to the web map, and these geo-texts reduce utility to users. Therefore, this study proposes a method to detect uncorrelated geo-text annotations based on Voronoi k-order neighborhood partition and auto-correlation statistical models. On the basis of the geo-text classification and semantic vector transformation, a quantitative description method for spatial autocorrelation was established by the Voronoi weighting method of inverse vicinity distance. The Voronoi k-order neighborhood self-growth strategy was used to detect the minimum convergence neighborhood for spatial autocorrelation. The Pearson method was used to calculate the correlation degree of the geo-text in the convergence region and then deduce the type of geo-text to be filtered. Experimental results showed that for given geo-text types in the study region, the proposed method effectively calculated the correlation between new geo-texts and the convergence region, providing an effective suggestion for preventing uncorrelated geo-text from uploading to the web map environment.

Unstructured geo-text annotations volunteered by users of web map services enrich the basic geographic data. However, irrelevant geo-texts can be added to the web map, and these geo-texts reduce utility to users. Therefore, this study proposes a method to detect uncorrelated geo-text annotations based on Voronoi k-order neighborhood partition and auto-correlation statistical models. On the basis of the geo-text classification and semantic vector transformation, a quantitative description method for spatial autocorrelation was established by the Voronoi weighting method of inverse vicinity distance. The Voronoi k-order neighborhood self-growth strategy was used to detect the minimum convergence neighborhood for spatial autocorrelation. The Pearson method was used to calculate the correlation degree of the geo-text in the convergence region and then deduce the type of geo-text to be filtered. Experimental results showed that for given geo-text types in the study region, the proposed m...

En este estudio se realiza una primera aproximación cuantitativa a la escritura poética de autoría femenina en el Siglo de Oro mediante la aplicación de metodologías computacionales y estilométricas, siguiendo la estela de trabajos internacionales de gran relevancia en el campo de la Estilística computacional. Los resultados obtenidos apuntan a que es posible diferenciar la escritura femenina de la masculina en este género y periodo, y se ha podido detectar un motivo metapoético en la poesía de estas autoras relacionado con la escritura femenina.

This paper discusses approaches that promote the ability of Japanese language learners to carry out bottom-up processing when reading texts. Chunking and reading aloud have been suggested to automate bottom-up processing skills and are thought to facilitate bottom-up processing as follows: 1) Chunking or word segmentation is an activity that helps students to recognize word clusters and narrow their visual range through the practice of separating strings of letters into shorter words. This process is expected to prevent any misrecognition of letters and words; 2) Chunk reading involves processing on a multiword unit-by-multiword unit basis rather than a sentence-by-sentence basis, which reduces the cognitive load of processing in longer sentences with complex syntactic structures; 3) Chunk reading is a method of reading using the word order of the target language, which increases reading speed by preventing return reading; 4) Reading aloud is a language activity that links the written word to its pronunciation. Reading aloud speeds up the process of converting written words into phonemes. When phonological encoding is accelerated, word recognition, a lower processing task, becomes automatic; 5) Reading aloud is an activity that makes the word-internal repetitions that take place in the mind and promotes the speeding up of word-internal repetitions. When word-internal repetition is accelerated, the capacity for processing resource allocation is increased and the surplus can be allocated to content comprehension.

Datafication, or the translation of our everyday actions into quantifiable
metrics, underwrites a wide set of contemporary discursive practices. With
a specific focus on the social media platform Instagram, this paper analyzes
mediatized landscape signs as ‘datafied discourse’ enmeshed in an entangled
apparatus of platforms, algorithms, and online networks. Using a corpus of
404 public Instagram posts gathered from the Café de Flore geotag, I
examine how the vernacular practices of geotagging, mediatization, and
remediatization reflexively construct this ostensibly ‘user-generated’
landscape. I then consider the implications of these and other discursive
practices occurring at the ‘online-offline nexus’ through the dimensions of
authorship and ownership. Finally, amid a confluence of new LL work, I
propose to orient scholarship toward an ‘infrastructural’ perspective, in
which datafication, online platforms, and algorithms are understood to
exert considerable influence over our landscapes, thus emerging as relevant
to scholars engaged in any genre of LL analysis.

Astrobiology is often defined as the study of the origin, evolution, distribution and future of life on Earth and in the Universe and thought of as a discipline. In practice though, the delineation of astrobiology-related research and corresponding groups of researchers is far from straightforward. Here, we propose to apply text-mining methods to identify researcher communities depending on thematic similarities in their published works. After fitting a latent Dirichlet allocation topic model to the complete article corpus of three flagship journals in the field

Opposition is a core component of any democracy, yet it is scarcely studied. Leaning on research prescribing blurred lines between government and opposition in parliamentary democracies, we use word embeddings in tandem with sentiment analysis on written parliamentary questions in the Norwegian parliament to study conflict patterns between the government and opposition. Our findings consistently show that MPs of governing parties are more negative than MPs of the opposition. However, the effect is reduced considerably when the topical content of the question is included in the analysis. We attribute our finding to the existence of a participation threshold in parliamentary questions; MPs of governing parties will only ask questions whenever a given issue is sufficiently contentious.

This research uses artificial intelligence and manual content-analysis to examine the diffusion of incivility against political leaders on Twitter during the 2022 Italian election campaign. Using a mixed approach (artificial intelligence and manual content analysis), we examined 22,465 uncivil tweets posted in the 4 weeks before the vote. Results show that hostility toward leaders increases as voting approaches and as candidates' public visibility grows, and that it affects frontrunner leaders the most. Furthermore, the analysis of the different forms of incivility showed that it changes depending on the target it hits, revealing unexpected aspects: contrary to expectations, incivility against the only female leader (Giorgia Meloni) are not "sexist attacks" but forms of demonization (i.e., association with figures/symbols concerning totalitarian regimes); while against Giuseppe Conte, accusations of "illegality," lies and "misinformation" prevail, that is, the same kind of incivility that he and his party use against opponents. Finally, we found that the authors of uncivil attacks are mainly ordinary/sporadic users, with all the consequences that this implies in terms of a normalization of incivility in public debate.

This research paper explores India's speeches in United Nations General Debate (UNGD) sessions to investigate the nation's priority settings and challenges faced during the last five decades. The study adopted Latent Dirichlet Allocation (LDA), a computational linguistics technique to uncover hidden topics in a selected corpus. The data were accessed from the United Nations General Debate Corpus. The research underpins Computational Grounded Theory, which relies on the computational exploration of the text. The results reveal that world peace, nation-building, poverty eradication, economic development, women empowerment, dialogue to resolve issues, and terrorism as the global threat are the main issues and challenges faced by India. Through topic modeling and word clustering, it becomes clear that India wants to eradicate poverty and terrorism through the joint effort of the whole world  .

Spectral topic modeling algorithms operate on matrices/tensors of word co-occurrence statistics to learn topic-specific word distributions. This approach removes the dependence on the original documents and produces substantial gains in efficiency and provable topic inference, but at a cost: the model can no longer provide information about the topic composition of individual documents. Recently Thresholded Linear Inverse (TLI) is proposed to map the observed words of each document back to its topic composition. However, its linear characteristics limit the inference quality without considering the important prior information over topics. In this paper, we evaluate Simple Probabilistic Inverse (SPI) method and novel Prior-aware Dual Decomposition (PADD) that is capable of learning document-specific topic compositions in parallel. Experiments show that PADD successfully leverages topic correlations as a prior, notably outperforming TLI and learning quality topic compositions comparab...

This study compares and contrasts the results of two lexical-based methods aimed at identifying content temporal trends in diachronic text corpora. A corpus of end-of-year addresses of the presidents of the Italian Republic constitutes a relevant case of political speech useful to understand how the temporal evolution of topics can be represented and whether a downward (ex post) or an upward (ex ante) extraction of topics is more effective for the identification of presidents' distinctive traits and trends. The first method is a knowledgebased system (KBS), which identifies clusters of words sharing a similar temporal pattern through a three-step statistical learning procedure. The second is a structural topic model (STM), which identifies main topics by probing the possible effect of the year and president factors on the speech-topic and the topic-word distributions. In KBS clusters, the individual trait of the president stands out as one of the most relevant elements and determines the contents of speeches; moreover, topic trends can also be discerned ex post while interpreting the results. On the other hand, STM directly achieves the whole topic structure but seems not as powerful as expected in portraying the life cycle of words and detecting groups of words that distinguish the speeches of a specific president. As most presidential speeches are rich and cover a wide range of topics, the results suggest that, in this case, the interpretative tool offered by STM brings out more challenges than strengths. Conversely, direct observation of the temporal trajectory of individual words allows for more detailed analyses and meaningful results, thanks to the flexible and adaptive KBS approach.

Scatter/Gather systems are increasingly becoming useful in browsing document corpora. Usability of the present-day systems are restricted to monolingual corpora, and their methods for clustering and labeling do not easily extend to the multilingual setting, especially in the absence of dictionaries/machine translation. In this paper, we study the cluster labeling problem for multilingual corpora in the absence of machine translation using comparable corpora. Using a variational approach, we show that multilingual topic models can effectively handle the cluster labeling problem, which in turn allows us to design a novel Scatter/Gather system ShoBha. Experimental results on three datasets, namely the Canadian Hansards corpus, the entire overlapping Wikipedia of English, Hindi and Bengali articles, and a trilingual news corpus containing 41,000 articles, confirm the utility of the proposed system.

Topic models (TM), as introduced by Blei et al. (2003), are techniques used to infer linguistic themes from the co-occurrence of words in various textual entities, such as documents, paragraphs, sentences, or tweets. Traditionally, the process by which TMs derive these themes has been data-driven and unsupervised, meaning that human interpretative analysis typically occurs only after the algorithm has processed the data. This approach limits human intervention in determining the specific topics uncovered by the algorithm, as the initial modeling process is purely computational. In other words, with standard TMs, human interpretation serves to relate the output of the model to linguistically or sociologically relevant themes only after the unsupervised, data-driven modeling is complete. This secondary step involves evaluating the relevance and coherence of the topics identified by the algorithm, often through a detailed inspection of the model results (Bodell et al., 2022).

A major challenge for many analyses of Wikipedia dynamics-e.g., imbalances in content quality, geographic differences in what content is popular, what types of articles attract more editor discussionis grouping the very diverse range of Wikipedia articles into coherent, consistent topics. This problem has been addressed using various approaches based on Wikipedia's category network, WikiProjects, and external taxonomies. However, these approaches have always been limited in their coverage: typically, only a small subset of articles can be classified, or the method cannot be applied across (the more than 300) languages on Wikipedia. In this paper, we propose a language-agnostic approach based on the links in an article for classifying articles into a taxonomy of topics that can be easily applied to (almost) any language and article on Wikipedia. We show that it matches the performance of a language-dependent approach while being simpler and having much greater coverage. • Human-centered computing → Empirical studies in collaborative and social computing.

This article addresses the need for a comprehensive understanding of the rapidly evolving field of Artificial Intelligence (AI) in education, given its potential to transform teaching and learning practices. The study analyzed 1,234 articles from the Web of Science database, using bibliometric techniques and topic modeling. Quantitative analyses of publication trends, citation impacts, and collaboration patterns were conducted using the R programming language, and Latent Dirichlet Allocation (LDA) was employed to uncover latent themes and potential research gaps. The study reveals a dramatic growth in research output, with an annual growth rate of 47.9%. China and the United States emerge as dominant contributors, collectively accounting for 38% of publications. Key research themes include AI in language learning, AI ethics and policy, and AI literacy. The findings highlight the need for more inclusive and diverse research efforts to address the unique challenges and opportunities of AI in education in across socioeconomic contexts.

This paper introduces a multimodal discussion corpus for the study into head movement and turn-taking patterns in debates. Given that participants either acted alone or in a pair, cooperation and competition and their nonverbal correlates can be analyzed. In addition to the video and audio of the recordings, the corpus contains automatically estimated head movements, and manual annotations of who is speaking and who is looking where. The corpus consists of over 2 hours of debates, in 6 groups with 18 participants in total. We describe the recording setup and present initial analyses of the recorded data. We found that the person who acted as single debater speaks more and also receives more attention compared to the other debaters, also when corrected for the time speaking. We also found that a single debater was more likely to speak after a team debater. Future work will be aimed at further analysis of the relation between speaking and looking patterns, the outcome of the debate and perceived dominance of the debaters.

The rapid development of AI, particularly in the form of language models such as OpenAI's ChatGPT, is fundamentally transforming the academic and educational landscape. AI tools like ChatGPT play a crucial role in accelerating text generation, enhancing writing processes, assisting in literature reviews, and facilitating personalized learning. The development and adoption of Generative Pre-trained Transformers (GPT), including ChatGPT, have sparked widespread interest due to their unprecedented capabilities in generating human-like text and automating communication. These advancements have a significant impact on various fields, including education and research, where AI-driven tools can optimize academic work and improve accessibility to knowledge. However, the integration of AI into academia comes with several serious challenges, such as algorithmic bias, data privacy concerns, ethical issues including plagiarism, and the potential decline in critical thinking skills due to excessive reliance on AI-generated content. While some educators and researchers view ChatGPT as a powerful tool for innovation, others express concerns about its impact on academic integrity and analytical reasoning. This review also compares ChatGPT with other advanced AI models, such as GPT-3 and BERT, highlighting their respective roles in science and education. An analysis of 159 independent literature sources, including 36 recently published papers on arXiv.org platform, indicates that interest in ChatGPT and its analogs in the field of science and education is rapidly growing. These studies emphasize the importance of AI in optimizing academic activities, improving knowledge accessibility, and developing new learning methods. The article concludes by discussing the future evolution of AI in academia, its integration with emerging technologies, and its potential for fostering interdisciplinary research in an era often described as the ʺnew AI gold rush.ʺ

Most of the relevance feedback algorithms only use document terms as feedback (local features) in order to update the query and re-rank the documents to show to the user. This approach is limited by the terms of those documents without any global context. We propose to use statistical topic modeling techniques in relevance feedback to incorporate a better estimate of context by including global information about the document. This is particularly helpful for difficult queries where learning the context from the interactions with the user is crucial. We propose to use the topic mixture information obtained to characterize the documents and learn their topics. Then, we rank documents incorporating positive and negative feedback by fitting a latent distribution for each class of documents online and combining all the features using Bayesian Logistic Regression. We show results using the OHSUMED dataset for 3 different variants and obtain higher performance, up to 12.5% in Mean Average Precision (MAP).

Since its launching year of 2022, ChatGPT has really become a phenomenon. Either with or without empirical evidence, many people believe it has potential to replace intellectual workers in many fields. The advent and development of ChatGPT has definitely facilitated more autonomous education, especially in the field of learning English in general and obtaining the knowledge to score higher in the IELTS exam in particular.

As an IELTS teacher, I have seen more and more people, including myself, turning to this tool as a way to evaluate and improve their speaking and writing capabilities. I have also leveraged this tool professionally to increase my work productivity such as in grading students’ essays. However, that leaves me with a big question of how effective ChatGPT is in essay grading and evaluation, when stacked up against its human counterparts. Therefore, in the scope of this paper, I will present the development of AES – automated essay scoring technologies, ChatGPT and most importantly review three studies that I have read that provide me with valuable insights into the aforementioned question.

Conclusion
AI, despite having shown noticeable agreement with humans in essay evaluation, is not yet a mature tool to be wielded alone, especially for assessment of long essays in higher education or final assessment. ChatGPT should only be used for low-stakes assignments in a classroom, such as giving a preliminary grade on a first draft.

Research Problem: Islamic ideas on rulership and governance are disseminated through various textual traditions, among which the mirrors for princes genre holds a prominent place. These texts, serving as manuals for rulers and future leaders, encapsulate a blend of ethical, spiritual, and practical guidance. However, there is a lack of clarity regarding the nature of this genre, particularly its integration of religious and political dimensions, as well as its significance in shaping Islamic thought on governance. Research Purposes: This study aims to explore the historical and cultural development of the mirrors for princes genre, analyze its content to uncover insights into Islamic rulership, and challenge the notion that this genre is quasi-secular by highlighting its intrinsic connection to Islamic principles. Research Methods: The research employs a qualitative approach, drawing on a comprehensive review of primary and secondary sources within the mirrors for princes tradition. Historical analysis and textual interpretation are utilized to examine the themes, structures, and intended purposes of these works across different Islamic contexts. Results and Discussion: The study reveals that mirrors for princes are deeply embedded in Islamic ethical and political thought, offering rulers guidance that intertwines religious obligations with administrative responsibilities. These texts emphasize the ruler's role as a moral and spiritual guide, as well as a political leader. The research demonstrates the diversity of traditions that contributed to the genre and highlights its adaptability across time and regions in the Islamic world. Research Implications and Contributions: This study contributes to a deeper understanding of Islamic political and ethical thought, emphasizing the mirrors for princes genre as a vital source of historical insights into governance. It challenges secular interpretations by illustrating the genre's rootedness in Islamic values and its role in shaping the ideals of rulership. The findings provide a foundation for further research into the interplay between religion and politics in Islamic history and offer valuable perspectives for comparative studies in governance and leadership.

Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and Diffusion Models have recently gained widespread attention from both the research and the industrial communities. This survey explores their application in network monitoring and management, focusing on prominent use cases, as well as challenges and opportunities. We discuss how network traffic generation and classification, network intrusion detection, networked system log analysis, and network digital assistance can benefit from the use of GenAI models. Additionally, we provide an overview of the available GenAI models, datasets for largescale training phases, and platforms for the development of such models. Finally, we discuss research directions that potentially mitigate the roadblocks to the adoption of GenAI for network monitoring and management. Our investigation aims to map the current landscape and pave the way for future research in leveraging GenAI for network monitoring and management.

The study offers a comprehensive analysis of global research on Chatbot and ChatGPT from 2002 to 2023. A rapid research growth has been noted after the year 2017. The research growth, authorship analysis, keyword analysis, citation pattern, co-occurrence of keywords and thematic analysis were analysed. The USA leads in publications and citations, while the Norway achieved the highest average citations. Keyword trends highlighted "Chatbot," "Artificial intelligence" and "ChatGPT" as research domains. Thematic clusters emerged from co-occurrence analysis. Finally, this study maps the dynamic evolution of Chatbot and ChatGPT research in the first two decades of the 21 st century and offers insights for researchers, practitioners and policymakers in the AI research field.

This paper aims to explore the integration of Systematic Inventive Thinking (SIT) methodology with Large Language Models (LLMs) to enhance innovative processes. It seeks to assess how LLMs can support analytical and creative processes in design teams and how hybrid human-LLM collaboration can contribute to more dynamic and unconventional problem-solving approaches.
Design/methodology/approach: The study employs a theoretical analysis of SIT methodology and LLM capabilities, synthesizing existing literature on both topics. It proposes a framework for integrating SIT with LLMs, including structured prompt patterns for each stage of the SIT process. The approach includes a comparative analysis of human and LLM capabilities in inventive processes.
Findings: Research reveals that LLMs can significantly enhance the SIT process by providing rapid information synthesis, generating diverse ideas, and systematically applying SIT principles. However, human creativity, intuition, and holistic assessment remain crucial for breakthrough innovations. The study identifies specific prompt patterns and techniques for effective human-LLM collaboration within the SIT framework.
Research limitations/implications: As this is an initial theoretical framework, empirical validation through case studies or experimental research is needed to assess its practical effectiveness.
Practical implications: The proposed framework offers practitioners in the fields of innovation and design a structured approach to integrating AI into their creative processes. Provides specific guidelines for the use of LLM to enhance each stage of the SIT methodology, which could lead to more efficient and innovative outcomes.
Social implications: Integration of SIT with LLM could significantly influence public attitudes toward AI, potentially increasing its acceptance as a collaborative tool in creative and problemsolving processes. This approach may lead to more efficient and sustainable innovation practices in various industries, potentially addressing social challenges more effectively. However, it may also raise concerns about job displacement in creative fields, necessitating a focus on reskilling and education to prepare the workforce for collaboration with AI systems.
Originality/value: This paper presents a novel approach to integrating SIT methodology with state-of-the-art AI technology, offering new perspectives on increasing human creativity with machine capabilities in structured innovation processes. It contributes to the emerging field of AI-assisted design thinking and provides a foundation for further research in this area.

Topic modeling (TM) is an unsupervised technique used to recognize hidden or abstract topics in large corpora, extracting meaningful patterns of words (semantics). This paper explores TM within data mining (DM), focusing on challenges and advancements in extracting insights from datasets, especially from social media platforms (SMPs). Traditional techniques like latent Dirichlet allocation (LDA), alongside newer methodologies such as bidirectional encoder representations from transformers (BERT), generative pre-trained transformers (GPT), and extra long-term memory networks (XLNet) are examined. This paper highlights the limitations of LDA, prompting the adoption of embedding-based models like BERT and GPT, rooted in transformer architecture, offering enhanced context-awareness and semantic understanding. The paper emphasizes leveraging pre-trained transformer-based language models to generate document embedding, refining TM and improving accuracy. Notably, integrating BERT with XLNet summaries emerges as a promising approach. By synthesizing insights, the paper aims to inform researchers on optimizing TM techniques, potentially shifting how insights are extracted from textual data.

The history of ideas emerged as a distinct discipline in the 1940s under the leadership of Arthur Lovejoy, whose foundational principles emphasized meticulous qualitative analysis and the interpretation of selected thinkers' works. However, the increasing volume of authors and publications in recent decades has introduced significant challenges to the field. This article argues that incorporating quantitative methods can help address these challenges. Specifically, it explores the potential of topic modelling as a tool for studying ideas, highlighting its advantages and providing an example of its application through an analysis of sociological articles published in two leading journals: the American Journal of Sociology in the United States and Studia Socjologiczne in Poland.

In the face of the unprecedented COVID-19 pandemic, various government-led initiatives and individual actions (e.g., lockdowns, social distancing, and masking) have resulted in diverse pandemic experiences. This study aims to explore these varied experiences to inform more proactive responses for future public health crises. Employing a novel "bigthick" data approach, we analyze and compare key pandemic-related topics that have been disseminated to the public through newspapers with those collected from the public via interviews. Specifically, we utilized 82,533 U.S. newspaper articles from January 2020 to December 2021 and supplemented this "big" dataset with "thick" data from interviews and focus groups for topic modeling. Identified key topics were contextualized, compared and visualized at different scales to reveal areas of convergence and divergence. We found seven key topics from the "big" newspaper dataset, providing a macro-level view that covers public health, policies and economics. Conversely, three divergent topics were derived from the "thick" interview data, offering a micro-level view that focuses more on individuals' experiences, emotions and concerns. A notable finding is the public's concern about the reliability of news information, suggesting the need for further investigation on the impacts of mass media in shaping the public's perception and behavior. Overall, by exploring the convergence and divergence in identified topics, our study offers new insights into the complex impacts of the pandemic and enhances our understanding of key issues both disseminated to and resonating with the public, paving the way for further health communication and policy-making.

For more than a year and a half, artificial intelligence has once again captured the imagination of both regular users and researchers worldwide. A pivotal moment was the release of the chatbot called ChatGPT to a broad audience on 30 November 2022. The tool, based on generative artificial intelligence and simulating written conversa-tion with users, was capable of, among other things, conducting discourse, answering various questions, including those encyclopaedic in nature, writing, summarizing, and translating various texts, performing calculations, creating educational materials, etc. Soon after this even, the chatbots from other companies emerged, followed by a massive number of other AI-powered tools. Naturally, the academic community enthusiastically analysed the possibilities of using these new solutions in scientific work. Due to the nature of chatbots, they are tools closely related to the humanities, particularly to applied linguistics; the deep learning models used in these chatbots rely on large text corpora, while the queries (or instructions, called prompts) are formulated by users in a natural language. Needless to say that the technology can also contribute to the broadly understood humanities, serving as an aid in many fields of scientific and practical activity. This is illustrated in the text with examples of specific studies. To better understand the technol-ogy discussed here for more effective application and to address the emerging doubts regarding its nature (including its capabilities), this chapter presents and explicates a series of concepts related to generative artificial intelligence. Key concepts are highlighted in the text (by way of bold font), and numerous footnotes refer the reader to additional materials on the discussed topics.

With Generative AI's (GenAI) rapid development and the ability to generate sophisticated human-like text, it has evolved as a powerful technology in various domains. However, its application in the education domain was initially met with resistance due to concerns about disrupting traditional learning and assessment methods, raising questions about academic integrity, and provoking ethical dilemmas related to data privacy and bias. Many schools, higher educational institutions, and governments initially chose to ban the use of GenAI tools due to the disruptions they caused to learning and teaching practices, only to rescind their bans later. This study conducts a literature review to investigate GenAI tools from the perspectives of key stakeholders in the educational domainstudents, educators, and administrators-highlighting their benefits while identifying challenges and limitations. The review found several benefits of using GenAI, such as personalised learning, immediate support, language support, and reduced administrative workload. This paper also provides usage guidelines for stakeholders and outlines future research areas to support GenAI adoption in higher education. Our findings indicate that most studies involving students had a positive view of using GenAI. There is a noticeable gap in research focusing on administrators, highlighting the need for further investigation.

Son yıllarda, farklı konular için sunulan dijital bilgi kaynaklarının sayısı aşırı miktarda artmaktadır. Bu dijital bilgi kaynaklarına erişim desteği sunan sistemlerin birçoğu tarama, arama ve bilgi geri kazanımı araçlarına odaklanmıştır. Sayısal kütüphaneler, elektronik kitaplıklar ve Web sayfaları, bilgi erişimini iyileştirmek, belge koleksiyonlarını farklı anahtar kriterlere göre hiyerarşik olarak oluşturmak ve düzenlemek için yeni birçok açılım sunmaktadır. Farklı arama araçları, bilgi erişim teknikleri kullanılarak erişilebilen belgeleri düzenlemek, endekslemek ve özetlemek için yazılım tabanlı hizmetleri kullanarak daha kapsamlı bir doküman kapsamı sunulabilmektedir. Dijital kütüphanelerdeki arama mekanizmalarına uygulanan teknolojiler, doküman koleksiyonlarını yönetmek, anlamlı veri çıkarmak ve doküman ilişkilerinin belirlenmesi için farklı yöntem ve teknolojilerin kullanımını zorunlu kılmıştır. Özellikle belgeler arasındaki ilişki ne biçimleri ne de türleri ile açıkça tanıml...

Traditional works in sentiment analysis and aspect rating prediction do not take author preferences and writing style into account during rating prediction of reviews. In this work, we introduce Joint Author Sentiment Topic Model (JAST), a generative process of writing a review by an author. Authors have di�fferent topic preferences, `emotional' attachment to topics, writing style based on the distribution of semantic (topic) and syntactic (background) words and their tendency to switch topics. JAST uses Latent Dirichlet Allocation to learn the distribution of author-speci�fic topic preferences and emotional attachment to topics. It uses a Hidden Markov Model to capture short range syntactic and long range semantic dependencies in reviews to capture coherence in author writing style. JAST jointly discovers the topics in a review, author preferences for the topics, topic ratings as well as the overall review rating from the point of view of an author. To the best of our knowledge...

Traditional works in sentiment analysis and aspect rating prediction do not take author preferences and writing style into account during rating prediction of reviews. In this work, we introduce Joint Author Sentiment Topic Model (JAST), a generative process of writing a review by an author. Authors have different topic preferences, 'emotional' attachment to topics, writing style based on the distribution of semantic (topic) and syntactic (background) words and their tendency to switch topics. JAST uses Latent Dirichlet Allocation to learn the distribution of author-specific topic preferences and emotional attachment to topics. It uses a Hidden Markov Model to capture short range syntactic and long range semantic dependencies in reviews to capture coherence in author writing style. JAST jointly discovers the topics in a review, author preferences for the topics, topic ratings as well as the overall review rating from the point of view of an author. To the best of our knowledge, this is the first work in Natural Language Processing to bring all these dimensions together to have an author-specific generative model of a review.

64  Abstract:-We present the details of a large scale user profiling framework that we developed here on Apache Hadoop. We address the problem of extracting and maintaining a very large number of user profiles extracted from large scale data. In this work, a user profiles is often used to classify a given user into pre-defined user segments or to capture the online behavior of the user including the user’s private interests and preferences. A user profiles can be explicitly defined by the user himself. User Profiling is usually defined as the process of implicitly learning a user profiles from data associated with the user. The Data extracted in stored form of the xlsx, pdf, docx format in certain Data-marts or organization is also extracted to get user information and personalize the user’s behavior accordingly. Data sources for user profiling include among others the user’s browsing sessions or even other user profiles using collaborative filtering techniques.

Topic modelling (TM) is a significant natural language processing (NLP) task and is becoming more popular, especially, in the context of literature synthesis and analysis. Despite the growing volume of studies on the use of and versatility of TM, the knowledge of TM development, especially from the perspective of bibliometrics analysis is limited. To this end, this study evaluated TM research using two techniques namely, bibliometrics analysis and TM itself to provide the current status and the pathway for future studies in the TM field. For this purpose, this study used 16,941 documents collected from Scopus database from 2004 to 2023. Results indicate that the publications on TM have increased over the years, however, the citation impact has declined. Furthermore, the scientific production on TM is concentrated in two countries namely, China and the USA. Our findings showed there are several applications of TM that are understudied, for example, TM for image segmentation and classification. This paper highlighted the future research directions, most importantly, calls for increased multidisciplinary research approaches to fully deploy TM algorithms optimally and thus, increase usage in non-computer science subject areas.