arXiv cs.CY· Shimaa Ibrahim, Nour Elhouda Dalloua, Md. Rafiul Biswas, Wajdi Zaghouani·· 12 小时前AI 评分20
AraEngage：阿拉伯语 Facebook 新闻互动度的可解释语言特征
AraEngage: Interpretable Linguistic Correlates of Engagement in Arabic Facebook News
AI 导读
AraEngage 构建了覆盖 57 个国家、2451 个页面的 1 万条阿拉伯语 Facebook 新闻帖样本，将互动量与可解释语言特征配对。Random Forest 模型在帖子级划分上 R²=0.38，宗教表达、消息长度、句子长度和词汇密度是主要相关因素，且正向情感在该样本中与更高互动相关，与英文新闻不同。
正文 · 原文
Abstract:Engagement features developed for English are routinely applied to Arabic, but unvocalized Arabic changes what a lexical feature measures. We introduce AraEngage, a query-defined sample of 10,000 public Arabic Facebook news posts from 2,451 pages in 57 countries (2023-2024) that pairs engagement counts with interpretable linguistic features. We use it to examine which interpretable Arabic indicators are associated with follower-normalized engagement, and how far those associations can be trusted given publisher context, the definition of engagement, and lexical ambiguity. Religious expressions, message length, sentence length, and lexical density are the leading correlates in a Random Forest model ($R^2$ = 0.38 on a post-level split) interpreted with SHAP. Unlike English news, positive sentiment is associated with higher engagement in this Arabic news sample. Message length shows a length paradox: unadjusted raw engagement is highest for long posts, whereas the model's attributions for follower-normalized engagement favor short posts. A linguistic audit identifies why standard lexicon features are fragile in unvocalized Arabic: imperative lists contain homographs such as the form read as "express!" that is also the frequent preposition "via", hedge lists conflate the possibility marker qad + imperfect ("may") with the emphatic qad + perfect ("has indeed"), and religious lists mix devotional formulas with place names in political news. These are plausible sources of measurement error that may attenuate the near-zero associations of imperatives and hedges. All findings are observational associations with operationalized indicators. We release the derived linguistic features, engagement counts, refined lexicons, and analysis code; raw posts are not redistributed, in line with CrowdTangle terms.
| Comments: | 13 pages, 4 figures, 8 tables. To appear in ArabicNLP 2026 |
| Subjects: | Computation and Language (cs.CL); Computers and Society (cs.CY); Social and Information Networks (cs.SI) |
| Cite as: | arXiv:2611.07926 [cs.CL] |
| (or arXiv:2611.07926v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07926 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shimaa Ibrahim [view email]
[v1]
Thu, 5 Nov 2026 12:32:25 UTC (1,182 KB)
来源：arXiv cs.CY · arxiv.org