The study examines how Standard Arabic (SA) and Colloquial Arabic (CA) are distributed across X and Facebook by analyzing 10,000 public posts classified into seven discourse categories. Statistical tests reveal significant associations between discourse category and diglossic choice on both platforms, with platform also influencing diglossic choice. Logistic regression shows higher odds of SA use on X in certain domains, indicating that digital platforms reshape but do not erase diglossic boundaries.
By Fahad Al Hussen (King Saud University, Riyadh, Saudi Arabia), Mohammed Q. Shormani (Ibb University, Ibb, Yemen)
arXiv:2611.07926v1 Announce Type: new
Abstract: Engagement features developed for English are routinely applied to Arabic, but unvocalized Arabic changes what a lexical feature measures. We introduce...
By Shimaa Ibrahim, Nour Elhouda Dalloua, Md. Rafiul Biswas, Wajdi Zaghouani
arXiv:2608.21975v1 Announce Type: new
Abstract: This study examines the performance of the state-of-the-art MARBERT model in identifying the lexical/pragmatic category associated with emoji use on X...
By Mohammed Q. Shormani, Yehia A. AlSohbani, Mohammed Q. Shormani
arXiv:2608.23421v1 Announce Type: new
Abstract: Natural Language Processing (NLP) has grown rapidly over the past decade, driven by digital transformation in the Arab world, social media, and large l...
By Mullosharaf K. Arabov
Arabic Natural Language Processing (NLP) has grown rapidly over the past decade, driven by digital transformation in the Arab world, social media, and large language models (LLMs). Despite this growth...
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
arXiv:2610.19028v1 Announce Type: new
Abstract: Science fiction is widely credited with unsettling gender. This article asks whether that holds at the linguistic level, in how readily gendered refere...
By Yuxuan Liu, Haim Dubossarsky, Ruth Ahnert
The study introduces a unified method for measuring rhetorical distortion across six social media platforms and two languages, distinguishing between creator posts and their replies. It identifies five distortion dimensions—Significance Inflation, Anxiety Manufacturing, Novelty Claims, Loaded Language, and Temporal Distortion—using a three‑tier pipeline that combines lexical rules, negative filters, and LLM verification. Results show that distortion is far more strongly linked to the platform than content type, with X/Twitter exhibiting the highest distortion scores, and that the audience layer (replies) carries significantly more distortion than creator posts.
By Xucheng Yu, Jiangping Chen, Haohan Wang
arXiv:2607.02368v4 Announce Type: replace-cross
Abstract: Researchers are increasingly prompting language models with cultural personas so that they answer questionnaires as diverse human groups woul...
By Yuan Yuan
arXiv:2609.16006v1 Announce Type: cross
Abstract: Large language models (LLMs) increasingly serve users whose expectations are shaped by their cultural context, yet most cultural evaluations test wha...
By Enes Altinisik, Hamdy Mubarak, Masoomali Fatehkia, Husrev_Taha_Sencar Husrev Taha Sencar
arXiv:2609.22375v1 Announce Type: cross
Abstract: Introduction. Open collaborative knowledge graphs such as Wikidata increasingly ground agentic artificial intelligence, information retrieval, and la...
By Clair Kronk
The study examines how generative AI has reshaped the public web in under five years, using over 5 million crawlable pages. It finds that AI-generated text has risen sharply but unevenly, with higher prevalence on English-language and .com sites, lower on .gov and .edu, and varying by a page’s centrality in the web graph. The authors employ multiple detection tools and a Bayesian hierarchical model to estimate prevalence while accounting for detector errors.
By Adam Visokay, Soumyakanti Pan, Molly Griston, Polina Iasakova, Mark Nepf, Denis Peskoff, Riya Sinha, Jikai Sun, Isabelle Thiara, Letao Wang, Yiwei Wu, Lauren Yan, Tyler H. McCormick