This study highlights the distribution of Standard Arabic (SA; H(igh) variety) and Colloquial Arabic (CA; L(ow) variety) across X and Facebook. 16754 public posts were collected via Python, with 10000...
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
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
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...