The paper introduces a new method that uses embedding-based dynamic topic modeling to detect and measure topic drift at the comment level in a large dataset. By applying pretrained language models to generate contextualized embeddings for 12.7 billion Reddit comments from 2006 to 2022, the authors identify evolving topic clusters over time using unsupervised techniques. Their approach includes scalable modifications to existing methods and a null model comparison test, revealing that politically and socially contentious topics show significant directional drift, while areas like music and sports remain relatively stable.
Dynamic Embedded Topic Models (D-ETM) are extended to enable stable cross‑corpus temporal analysis by first learning a shared dynamic topic space—called the shared backbone—over a merged multi‑corpus collection. Corpus‑specific residual adaptation is then applied around this frozen backbone, allowing each corpus to specialize lexically without creating separate latent topic spaces. Experiments on three corpora spanning 97 years show that this approach yields much stronger alignment of topic trajectories (97.5 ± 0.7 % Retrieval@1) compared to full fine‑tuning or independent training with post‑hoc matching.
By Ruoxuan Li, Bruce Kogut
arXiv:2603.18358v2 Announce Type: replace
Abstract: Outliers in dynamic topic modeling are typically treated as noise, yet we show that some can serve as early signals of emerging topics. We introduc...
By Evangelia Zve, Gauvain Bourgne, Benjamin Icard, Jean-Gabriel Ganascia
TopiCLEAR is a framework that clusters document or sentence embeddings using adaptive dimensionality reduction to uncover low‑dimensional geometric structures that correspond to human‑interpretable topics. The method is evaluated on four benchmark datasets, showing strong agreement with human annotations, especially for short and informal texts. A Twitter case study demonstrates that TopiCLEAR yields more interpretable topics than LDA, recovering both annotated topic structure and coherent sub‑topics.
By Aoi Fujita, Taichi Yamamoto, Yuri Nakayama, Ryota Kobayashi
arXiv:2606.22342v2 Announce Type: replace
Abstract: How does research evolve, and can we trace it at the level of individual claims? Scientific progress is not simply a uniform accumulation of facts....
By Abdul Muntakim, Md Abdullah Al Hafiz Khan, Sadid Hasan, Yong Pei
arXiv:2510. 18908v2 Announce Type: replace-cross Abstract: Social media platforms such as Twitter (now X) provide rich data for analyzing public discourse, especially during crises such as the COVID-19 pandemic.
By Wangjiaxuan Xin, Shuhua Yin, Shi Chen, Yaorong Ge
The paper presents an end‑to‑end framework for extracting and clustering trilingual Sri Lankan parliamentary debates in Sinhala, Tamil, and English. Using LLM‑based text extraction, multilingual embeddings, and density‑based clustering, the authors recover 30 macro‑topics with a cluster purity of 0.673. The temporal patterns of these topics align with major national events such as the 2019 Easter attacks and the 2022 economic crisis, demonstrating the method’s effectiveness where traditional LDA fails.
By Himath Dhanapala, Haren Daishika, Himandhi Kuruppu, Sithija Seneviratne, Ashini Kavindya, Patalee Narasinghe, Sandeepa Weerasekara, Nisansa de Silva, Sandareka Wickramanayake
TTGBench is a new benchmark for temporal graph learning that evaluates both structural evolution and semantic drift in text‑attributed graphs. It includes six real‑world, text‑rich datasets with dual volatility and supports multi‑class and multi‑label temporal node classification, addressing gaps left by existing benchmarks. A comprehensive evaluation of 17 state‑of‑the‑art methods shows a clear divide: TGNNs excel at structural prediction but struggle with semantic tracking, while LLM‑based models perform better on semantic tasks but lag in structural prediction.
By Longfei Ma, Zemin Liu, Fei Wu
The paper introduces TSN4PI, a unified framework for tracking the evolution of political ideologies on social media. It combines a PIDN module that uses large language models, style transfer, and unsupervised domain adaptation to detect ideologies and filter noise, with a PIPN module that employs temporal graph neural networks to predict future ideological shifts. The authors release two large-scale datasets and validate the approach on platforms such as X and Truth Social, offering empirical insights into political polarization and ideology evolution.
The paper introduces C$^{3}$T, a Counterfactual Causal Conversation Transformer that models sentiment shifts in social‑media conversation trees. It treats discourse moves such as denial, evidence, and toxicity as interventions, predicts node sentiment and shifts, and attributes sentiment changes to specific ancestor messages. The authors also present CaSiRe, a causal sentiment reasoning layer that enriches rumor conversation datasets with sentiment, shift, intervention, and causal‑source annotations, and demonstrate that C$^{3}$T outperforms baseline models in robustness and interpretability.
By S M Rafiuddin, Atriya Sen
The paper introduces TSN4PI, a unified framework for tracking the evolution of political ideologies on social media. It combines a PIDN module that uses large language models, style transfer, and unsupervised domain adaptation to detect ideologies and filter noise, with a PIPN module that employs temporal graph neural networks to predict future ideological shifts. The authors release two large-scale datasets and validate the approach on platforms such as X and Truth Social, offering empirical insights into political polarization and online ideology evolution.
By Yijie Xu, Chao Wang, Hui Xiong
arXiv:2510.15125v3 Announce Type: replace-cross
Abstract: Social media platforms play a pivotal role in shaping political discourse, but the scale and rapid evolution of online content make systemati...
By Alexander Brady, Tunazzina Islam