Tootfinder

XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments
Tien Phat Nguyen, Vu Minh Ngo, Tung Nguyen, Linh Van Ngo, Duc Anh Nguyen, Sang Dinh, Trung Le
https://arxiv.org/abs/2510.02788

XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments
Cross-lingual topic modeling aims to uncover shared semantic themes across languages. Several methods have been proposed to address this problem, leveraging both traditional and neural approaches. While previous methods have achieved some improvements in topic diversity, they often struggle to ensure high topic coherence and consistent alignment across languages. We propose XTRA (Cross-Lingual Topic Modeling with Topic and Representation Alignments), a novel framework that unifies Bag-of-Words …

Topic Modeling as Long-Form Generation: Can Long-Context LLMs revolutionize NTM via Zero-Shot Prompting?
Xuan Xu, Haolun Li, Zhongliang Yang, Beilin Chu, Jia Song, Moxuan Xu, Linna Zhou
https://arxiv.org/abs/2510.03174

Topic Modeling as Long-Form Generation: Can Long-Context LLMs revolutionize NTM via Zero-Shot Prompting?
Traditional topic models such as neural topic models rely on inference and generation networks to learn latent topic distributions. This paper explores a new paradigm for topic modeling in the era of large language models, framing TM as a long-form generation task whose definition is updated in this paradigm. We propose a simple but practical approach to implement LLM-based topic model tasks out of the box (sample a data subset, generate topics and representative text with our prompt, text assi…

Experimental Evaluation of Dynamic Topic Modeling Algorithms
Ngozichukwuka Onah, Nadine Steinmetz, Hani Al-Sayeh, Kai-Uwe Sattler
https://arxiv.org/abs/2508.00710 https://

Experimental Evaluation of Dynamic Topic Modeling Algorithms
The amount of text generated daily on social media is gigantic and analyzing this text is useful for many purposes. To understand what lies beneath a huge amount of text, we need dependable and effective computing techniques from self-powered topic models. Nevertheless, there are currently relatively few thorough quantitative comparisons between these models. In this study, we compare these models and propose an assessment metric that documents how the topics change in time.

GHTM: A Graph based Hybrid Topic Modeling Approach in Low-Resource Bengali Language
Farhana Haque, Md. Abdur Rahman, Sumon Ahmed
https://arxiv.org/abs/2508.00605 https://…

GHTM: A Graph based Hybrid Topic Modeling Approach in Low-Resource Bengali Language
Topic modeling is a Natural Language Processing (NLP) technique that is used to identify latent themes and extract topics from text corpora by grouping similar documents based on their most significant keywords. Although widely researched in English, topic modeling remains understudied in Bengali due to its morphological complexity, lack of adequate resources and initiatives. In this contribution, a novel Graph Convolutional Network (GCN) based model called GHTM (Graph-Based Hybrid Topic Model)…

Chronotome: Real-Time Topic Modeling for Streaming Embedding Spaces
Matte Lim, Catherine Yeh, Martin Wattenberg, Fernanda Vi\'egas, Panagiotis Michalatos
https://arxiv.org/abs/2509.01051

Chronotome: Real-Time Topic Modeling for Streaming Embedding Spaces
Many real-world datasets -- from an artist's body of work to a person's social media history -- exhibit meaningful semantic changes over time that are difficult to capture with existing dimensionality reduction methods. To address this gap, we introduce a visualization technique that combines force-based projection and streaming clustering methods to build a spatial-temporal map of embeddings. Applying this technique, we create Chronotome, a tool for interactively exploring evolving themes in t…

Fractal Flow: Hierarchical and Interpretable Normalizing Flow via Topic Modeling and Recursive Strategy
Binhui Zhang, Jianwei Ma
https://arxiv.org/abs/2508.19750 https://…

Fractal Flow: Hierarchical and Interpretable Normalizing Flow via Topic Modeling and Recursive Strategy
Normalizing Flows provide a principled framework for high-dimensional density estimation and generative modeling by constructing invertible transformations with tractable Jacobian determinants. We propose Fractal Flow, a novel normalizing flow architecture that enhances both expressiveness and interpretability through two key innovations. First, we integrate Kolmogorov-Arnold Networks and incorporate Latent Dirichlet Allocation into normalizing flows to construct a structured, interpretable lat…

On my way to devSec() in Regensburg to talk about formal methods.
One would think this would be a major topic, given the rising criticality of software systems, and the availability of techniques and tools for decades - but no.
https://www.heise-devsec.de/veranstaltun…

heise devSec – die Konferenz für sichere Software- und Webentwicklung
Sichere Software beginnt vor der ersten Zeile Code ... Schwerpunkte im Herbst 2025 sind GenAI und Vibe Computing, Secure Software Development Lifecycle (SSDL), KI in der Softwareentwicklung, DevSecOps, Threat Modeling und Software Supply Chain Security.

Opportunities and Challenges of Integrating ChatGPT in Education: Sentiment Analysis and Topic Modeling
Surat Teerakapibal, Poompak Kusawat
https://arxiv.org/abs/2508.16966 http…

Opportunities and Challenges of Integrating ChatGPT in Education: Sentiment Analysis and Topic Modeling
Since its recent debut, ChatGPT has become a global sensation and significantly impacted the field of education. Both educational researchers and practitioners have identified opportunities as well as risks associated with the use of this novel tool in educational settings. Despite the ongoing debate, there is still no research exploring occupational differences in the perception of ChatGPT in education. In this paper, we analyzed Twitter data using topic modeling and sentiment analysis to inve…

A Markovian Approach for Cross-Category Complementarity in Choice Modeling
Omar El Housni, Shuo Sun, Rajan Udwani
https://arxiv.org/abs/2508.18566 https://…

A Markovian Approach for Cross-Category Complementarity in Choice Modeling
While single-purchase choice models have been widely studied in assortment optimization, customers in modern retail and e-commerce environments often purchase multiple items across distinct product categories, exhibiting both substitution and complementarity. We consider the cross-category assortment optimization problem where retailers jointly determine assortments across categories to maximize expected revenue. Most prior work on the topic either overlooks complementarity or proposes models t…

Exploring Left-Wing Extremism on the Decentralized Web: An Analysis of Lemmygrad.ml
Utkucan Balci, Michael Sirivianos, Jeremy Blackburn
https://arxiv.org/abs/2507.23699 https://…

Exploring Left-Wing Extremism on the Decentralized Web: An Analysis of Lemmygrad.ml
This study investigates the presence of left-wing extremism on the Lemmygrad.ml instance of the decentralized social media platform Lemmy, from its launch in 2019 up to a month after the bans of the subreddits r/GenZedong and r/GenZhou. We conduct a temporal analysis on Lemmygrad.ml's user activity, with also measuring the degree of highly abusive or hateful content. Furthermore, we explore the content of their posts using a transformer-based topic modeling approach. Our findings reveal a subst…

Is Artificial Intelligence Reshaping the Landscape of the International Academic Community of Geosciences?
Liang Li, Yuntian Li, Wenxin Zhao, Shan Ye, Yun Lu
https://arxiv.org/abs/2508.20117

Is Artificial Intelligence Reshaping the Landscape of the International Academic Community of Geosciences?
Through bibliometric analysis and topic modeling, we find that artificial intelligence (AI) is positively transforming geosciences research, with a notable increase in AI-related scientific output in recent years. We are encouraged to observe that earth scientists from developing countries have gained better visibility in the recent AI for Science (AI4S) paradigm and that AI is also improving the landscape of international collaboration in geoscience-related research.

Piecing together formic acid isomerism in dark clouds. Detection of cis-formic acid in TMC-1 and astrochemical modeling
G. Molpeceres, M. Ag\'undez, M. Mallo, C. Cabezas, M. Sanz-Novo, V. M. Rivilla, J. Garc\'ia de la Concepci\'on, I. Jim\'enez-Serra, J. Cernicharo
https://arxiv.org/abs/2509.18315

Metasurface-based Fluid Antennas: from Electromagnetics to Communications Model
Pablo Ram\'irez-Espinosa, Cleof\'as Segura-G\'omez, \'Angel Palomares-Caballero, F. Javier L\'opez-Mart\'inez, David Morales-Jim\'enez
https://arxiv.org/abs/2507.17982

Metasurface-based Fluid Antennas: from Electromagnetics to Communications Model
Fluid antenna systems (FASs) have become a popular topic in the wireless community as an effective yet simple means of exploiting spatial diversity. Due to the limitations of physically moving radiating elements, electronically reconfigurable antennas are emerging as practical implementations of FASs, since changing the radiation pattern is functionally equivalent to physically moving the device. However, electronically reconfigurable antennas pose a challenge in terms of analytical modeling, o…

S2WTM: Spherical Sliced-Wasserstein Autoencoder for Topic Modeling
Suman Adhya, Debarshi Kumar Sanyal
https://arxiv.org/abs/2507.12451 https://

S2WTM: Spherical Sliced-Wasserstein Autoencoder for Topic Modeling
Modeling latent representations in a hyperspherical space has proven effective for capturing directional similarities in high-dimensional text data, benefiting topic modeling. Variational autoencoder-based neural topic models (VAE-NTMs) commonly adopt the von Mises-Fisher prior to encode hyperspherical structure. However, VAE-NTMs often suffer from posterior collapse, where the KL divergence term in the objective function highly diminishes, leading to ineffective latent representations. To miti…

Cross-Subreddit Behavior as Open-Source Indicators of Coordinated Influence: A Case Study of r/Sino & r/China
Manon Pilaud, Ian McCulloh
https://arxiv.org/abs/2507.16857 htt…

Cross-Subreddit Behavior as Open-Source Indicators of Coordinated Influence: A Case Study of r/Sino & r/China
This study investigates potential indicators of coordinated influence activity among users participating in both r/Sino and r/China, two ideologically divergent Reddit communities focused on Chinese political discourse. Topic modeling and sentiment analysis are applied to all posts and comments authored by dual-subreddit users to construct a user-topic sentiment matrix. Individual sentiment patterns are compared to global topic baselines derived from the broader r/Sino and r/China populations. …

Catching Dark Signals in Algorithms: Unveiling Audiovisual and Thematic Markers of Unsafe Content Recommended for Children and Teenagers
Haoning Xue, Brian Nishimine, Martin Hilbert, Drew Cingel, Samantha Vigil, Jane Shawcroft, Arti Thakur, Zubair Shafiq, Jingwen Zhang
https://arxiv.org/abs/2507.12571

Catching Dark Signals in Algorithms: Unveiling Audiovisual and Thematic Markers of Unsafe Content Recommended for Children and Teenagers
The prevalence of short form video platforms, combined with the ineffectiveness of age verification mechanisms, raises concerns about the potential harms facing children and teenagers in an algorithm-moderated online environment. We conducted multimodal feature analysis and thematic topic modeling of 4,492 short videos recommended to children and teenagers on Instagram Reels, TikTok, and YouTube Shorts, collected as a part of an algorithm auditing experiment. This feature-level and content-leve…

Agentic Retrieval of Topics and Insights from Earnings Calls
Anant Gupta, Rajarshi Bhowmik, Geoffrey Gunow
https://arxiv.org/abs/2507.07906 https://arxiv.org/pdf/2507.07906 https://arxiv.org/html/2507.07906
arXiv:2507.07906v1 Announce Type: new
Abstract: Tracking the strategic focus of companies through topics in their earnings calls is a key task in financial analysis. However, as industries evolve, traditional topic modeling techniques struggle to dynamically capture emerging topics and their relationships. In this work, we propose an LLM-agent driven approach to discover and retrieve emerging topics from quarterly earnings calls. We propose an LLM-agent to extract topics from documents, structure them into a hierarchical ontology, and establish relationships between new and existing topics through a topic ontology. We demonstrate the use of extracted topics to infer company-level insights and emerging trends over time. We evaluate our approach by measuring ontology coherence, topic evolution accuracy, and its ability to surface emerging financial trends.
toXiv_bot_toot

Matrix-variate integer-valued autoregressive processes
Nuo Xu, Kai Yang, Fukang Zhu
https://arxiv.org/abs/2509.07347 https://arxiv.org/pdf/2509.07347

Matrix-variate integer-valued autoregressive processes
In the fields of sociology and economics, the modeling of matrix-variate integervalued time series is urgent. However, no prior studies have addressed the modeling of such data. To address this topic, this paper proposes a novel matrix-variate integer-valued autoregressive model. The key techniques lie in defining two leftand right-matricial thinning operators. The probabilistic and statistical properties of the proposed model are investigated. Furthermore, two estimation methods are developed:…

Semantic-Augmented Latent Topic Modeling with LLM-in-the-Loop
Mengze Hong, Chen Jason Zhang, Di Jiang
https://arxiv.org/abs/2507.08498 https://

Semantic-Augmented Latent Topic Modeling with LLM-in-the-Loop
Latent Dirichlet Allocation (LDA) is a prominent generative probabilistic model used for uncovering abstract topics within document collections. In this paper, we explore the effectiveness of augmenting topic models with Large Language Models (LLMs) through integration into two key phases: Initialization and Post-Correction. Since the LDA is highly dependent on the quality of its initialization, we conduct extensive experiments on the LLM-guided topic clustering for initializing the Gibbs sampl…

Tides of Memory: Digital Echoes of Netizen Remembran
Lingyu Peng, Chang Ge, Liying Long, Xin Li, Xiao Hu, Pengda Lu, Qingchuan Li, Jiangyue Wu
https://arxiv.org/abs/2509.16579 h…

Tides of Memory: Digital Echoes of Netizen Remembran
This artwork presents an interdisciplinary interaction installation that visualizes collective online mourning behavior in China. By focusing on commemorative content posted on Sina Weibo following the deaths of seven prominent Chinese authors, the artwork employs data scraping, natural language processing, and 3D modeling to transform fragmented textual expressions into immersive digital monuments. Through the analysis of word frequencies, topic models, and user engagement metrics, the system …

The anatomy of Green AI technologies: structure, evolution, and impact
Lorenzo Emer, Andrea Mina, Andrea Vandin
https://arxiv.org/abs/2509.10109 https://ar…

The anatomy of Green AI technologies: structure, evolution, and impact
Artificial intelligence (AI) is a key enabler of innovation against climate change. In this study, we investigate the intersection of AI and climate adaptation and mitigation technologies through patent analyses of a novel dataset of approximately 63 000 Green AI patents. We analyze patenting trends, corporate ownership of the technology, the geographical distributions of patents, their impact on follow-on inventions and their market value. We use topic modeling (BERTopic) to identify 16 major …

Crosslisted article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[1/1]:
- Thematic and Task-Based Categorization of K-12 GenAI Usages with Hierarchical Topic Modeling
Schneider, Hasler, Varrone, Hoya, Schroffenegger, Mah, Peb\"ock

Scalable and consistent few-shot classification of survey responses using text embeddings
Jonas Timmann Mjaaland, Markus Fleten Kreutzer, Halvor Tyseng, Rebeckah K. Fussell, Gina Passante, N. G. Holmes, Anders Malthe-S{\o}renssen, Tor Ole B. Odden
https://arxiv.org/abs/2508.19836

Scalable and consistent few-shot classification of survey responses using text embeddings
Qualitative analysis of open-ended survey responses is a commonly-used research method in the social sciences, but traditional coding approaches are often time-consuming and prone to inconsistency. Existing solutions from Natural Language Processing such as supervised classifiers, topic modeling techniques, and generative large language models have limited applicability in qualitative analysis, since they demand extensive labeled data, disrupt established qualitative workflows, and/or yield var…

Distribution-free inference for LightGBM and GLM with Tweedie loss
Alokesh Manna, Aditya Vikram Sett, Dipak K. Dey, Yuwen Gu, Elizabeth D. Schifano, Jichao He
https://arxiv.org/abs/2507.06921

Distribution-free inference for LightGBM and GLM with Tweedie loss
Prediction uncertainty quantification is a key research topic in recent years scientific and business problems. In insurance industries (\cite{parodi2023pricing}), assessing the range of possible claim costs for individual drivers improves premium pricing accuracy. It also enables insurers to manage risk more effectively by accounting for uncertainty in accident likelihood and severity. In the presence of covariates, a variety of regression-type models are often used for modeling insurance clai…

DS@GT at LongEval: Evaluating Temporal Performance in Web Search Systems and Topics with Two-Stage Retrieval
Anthony Miyaguchi, Imran Afrulbasha, Aleksandar Pramov
https://arxiv.org/abs/2507.08360

DS@GT at LongEval: Evaluating Temporal Performance in Web Search Systems and Topics with Two-Stage Retrieval
Information Retrieval (IR) models are often trained on static datasets, making them vulnerable to performance degradation as web content evolves. The DS@GT competition team participated in the Longitudinal Evaluation of Model Performance (LongEval) lab at CLEF 2025, which evaluates IR systems across temporally distributed web snapshots. Our analysis of the Qwant web dataset includes exploratory data analysis with topic modeling over time. The two-phase retrieval system employs sparse keyword se…

DTECT: Dynamic Topic Explorer & Context Tracker
Suman Adhya, Debarshi Kumar Sanyal
https://arxiv.org/abs/2507.07910 https://arxiv…

DTECT: Dynamic Topic Explorer & Context Tracker
The explosive growth of textual data over time presents a significant challenge in uncovering evolving themes and trends. Existing dynamic topic modeling techniques, while powerful, often exist in fragmented pipelines that lack robust support for interpretation and user-friendly exploration. We introduce DTECT (Dynamic Topic Explorer & Context Tracker), an end-to-end system that bridges the gap between raw textual data and meaningful temporal insights. DTECT provides a unified workflow that sup…

'A Little Bubble of Friends': An Analysis of LGBTQ Pandemic Experiences Using Reddit Data
Dhruvee Birla, Nazia Akhtar
https://arxiv.org/abs/2507.15033

'A Little Bubble of Friends': An Analysis of LGBTQ+ Pandemic Experiences Using Reddit Data
Social media was one of the most popular forms of communication among young people with digital access during the pandemic. Consequently, crucial debates and discussions about the pandemic crisis have also developed on social media platforms, making them a great primary source to study the experiences of specific groups and communities during the pandemic. This study involved research using LDA topic modeling and sentiment analysis on data obtained from the social media platform Reddit to under…

HICode: Hierarchical Inductive Coding with LLMs
Mian Zhong, Pristina Wang, Anjalie Field
https://arxiv.org/abs/2509.17946 https://arxiv.org/pdf/2509.17946

HICode: Hierarchical Inductive Coding with LLMs
Despite numerous applications for fine-grained corpus analysis, researchers continue to rely on manual labeling, which does not scale, or statistical tools like topic modeling, which are difficult to control. We propose that LLMs have the potential to scale the nuanced analyses that researchers typically conduct manually to large text corpora. To this effect, inspired by qualitative research methods, we develop HICode, a two-part pipeline that first inductively generates labels directly from an…

Beyond the Silence: How Men Navigate Infertility Through Digital Communities and Data Sharing
Tawfiq Ammari, Zarah Khondoker, Yihan Wang, Nikki Roda
https://arxiv.org/abs/2509.10003

Beyond the Silence: How Men Navigate Infertility Through Digital Communities and Data Sharing
Men experiencing infertility face unique challenges navigating Traditional Masculinity Ideologies that discourage emotional expression and help-seeking. This study examines how Reddit's r/maleinfertility community helps overcome these barriers through digital support networks. Using topic modeling (115 topics), network analysis (11 micro-communities), and time-lagged regression on 11,095 posts and 79,503 comments from 8,644 users, we found the community functions as a hybrid space: informal dia…

Federal Reserve Communication and the COVID-19 Pandemic
Jonathan Benchimol, Sophia Kazinnik, Yossi Saadon
https://arxiv.org/abs/2508.04830 https://arxiv.or…

Federal Reserve Communication and the COVID-19 Pandemic
In this study, we examine the Federal Reserve's communication strategies during the COVID-19 pandemic, comparing them with communication during previous periods of economic stress. Using specialized dictionaries tailored to COVID-19, unconventional monetary policy (UMP), and financial stability, combined with sentiment analysis and topic modeling techniques, we identify a distinct focus in Fed communication during the pandemic on financial stability, market volatility, social welfare, and UMP, …