This paper compares community unionism in the National Boot and Shoe Union of the 1920s with Unite Community of the 2010s–2020s using BERTopic and cTF-IDF. It finds that Unite Community’s discourse aligns more with outward‑facing, social‑justice themes, whereas the Boot and Shoe Union focuses on internal administration, industrial relations, and member services. The study demonstrates how modern NLP can illuminate differences in historical labor archives and challenges assumptions about the continuity of community unionism.
arXiv:2608.30866v1 Announce Type: new Abstract: Building language technologies and conducting NLP research for low-resource languages---particularly when led by native speakers or involving participa...
Building language technologies and conducting NLP research for low-resource languages---particularly when led by native speakers or involving participatory research practices---are often framed as mea...
The paper presents a new computational framework for modelling organisation-level semantic identity using longitudinal textual data. It combines semantic representation learning, graph-based modelling, and temporal analysis to create interpretable semantic fingerprints that capture diversity, concentration, connectivity, novelty, and community composition. The framework is validated on a corpus of K‑pop lyrics from major South Korean entertainment companies, revealing distinct, evolving semantic identities that are statistically robust and reproducible.
The paper presents a quantitative method for analyzing historical change in Italian media by reconstructing a diachronic corpus of about 600,000 articles from "La Repubblica" (1985‑2000). Using NLP to examine lexical and semantic patterns, and tools from complex systems, the authors identify shifts in media discourse, such as Italy’s transition from the First to the Second Republic and major international conflicts like the Gulf and Kosovo Wars. They find that war periods correspond to lower semantic entropy, indicating a more focused discourse.
The paper introduces an unsupervised framework that identifies and characterizes competing narratives in political discourse on social media, specifically analyzing German politicians' tweets. It uses a multi‑stage pipeline incorporating topic modeling, event detection, and event linking to form coherent stories and reveal distinct user community perspectives. Two case studies on polarizing issues demonstrate the method’s effectiveness in uncovering divergent viewpoints and framing conflicts around trending political topics.
The Illinois Social Attitudes Aggregate Corpus (ISAAC) is an open, modular corpus comprising over 527 million English‑language Reddit posts from 2007 to 2023, curated for relevance to six social group distinctions—race, sexuality, age, ability, body weight, and skin tone. A multi‑step, human‑audited filtering pipeline keeps irrelevant content below 10% overall and per group, while each post receives algorithmic annotations of user home region and a suite of validated semantic labels such as moralization, sentiment, emotion, and linguistic generalization. ISAAC’s publicly available, reproducible pipeline enables cross‑category comparisons, high‑precision tracking of long‑term temporal shifts, and spatial mapping of public opinion and policy outcomes, and can be extended to new platforms, languages, and social categories via both point‑and‑click and programmatic interfaces.
arXiv:2601.17020v3 Announce Type: replace-cross Abstract: With the rising popularity of interdisciplinary work and increasing institutional incentives in this direction, there is a growing need to un...
arXiv:2601. 13317v2 Announce Type: replace-cross Abstract: Climate discourse online shapes public understanding of climate change and informs political and policy debate, yet it unfolds across structurally different environments: paid advertising platforms host targeted, institutionally produced messaging, while public social media reflects largely organic, user-driven discussion.
The paper introduces a transparent, hybrid framework for Quantitative Discourse Analysis that blends lexical and semantic techniques. Using custom Python pipelines with NLTK, spaCy, and Sentence Transformers, the authors fine‑tune preprocessing, lemmatisation, and embedding generation, then apply an iterative BERTopic workflow involving UMAP, HDBSCAN, and c‑TF‑IDF to produce coherent topics. The approach is illustrated through a historical political discourse case study, emphasizing code‑level transparency, researcher control, and methodological triangulation.
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.
The paper evaluates large language models (LLMs) for annotating German parliamentary debates on migration, achieving macro‑F1 scores comparable to human agreement, particularly with GPT‑5 and gpt‑oss‑120B. It combines soft‑label outputs with Design‑based Supervised Learning to mitigate systematic bias and applies the method to a 150‑plus‑year corpus, revealing high solidarity post‑war and a sharp rise in anti‑solidarity since 2015. The study demonstrates that LLMs can enable large‑scale social‑scientific analysis while highlighting the need for rigorous validation and bias correction.
The paper investigates how shared community affiliations, measured via Bluesky starter packs, correlate with common ground between users. By analyzing 191,648 user pairs, it finds that lexical similarity—used as a proxy for common ground—increases monotonically with the number of shared starter packs, especially when those packs represent distinct topical communities. The study also shows that this effect is independent of network proximity, indicating that community membership is a distinct, measurable carrier of common ground.