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...
By Nedjma Ousidhoum, Noopur Zambare, Mohamed Abdalla
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.
By Brinda Murali Krishna, Oktay Karaku\c{s}, Can Eyupoglu
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.
By Dario Zarcone, Salvatore Miccich\`e, David Sanchez
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.
By Sergej Wildemann, Erick Elejalde
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.
By Babak Hemmatian, Sarah Hadjarab, Jessica Chen, Benedek Kurdi