← 研踪 PaperTrailarXiv:2611.15408 · v1
AraCrypto-ML: A Multi-Label Corpus of Communicative Functions in Arabic Facebook Cryptocurrency Discourse
首次提交 2026-11-11 20:32
版本更新 2026-11-11 20:32
雷达观测 2026-11-13 11:11
摘要
Arabic cryptocurrency discourse on social media is communicatively heterogeneous: a single post may report prices, explain protocols, promote investment, warn of scams, or invoke Islamic jurisprudence. Yet no annotated resource captures this functional diversity. We introduce AraCrypto-ML, a 10,000-post multi-label corpus of Arabic Facebook cryptocurrency discourse (2014-2024) from 2,898 public pages using a set of related keywords. Collected posts are annotated into six categories: Market Commentary, Educational, Promotional, Skeptical-Warning, Religious Framing, and Other/Unclear, using Fanar-2-27B under a prompt-only multi-label protocol. We validate 1,000 posts against adjudicated gold labels (macro kappa = 0.74) and apply Rogan-Gladen calibration to correct labelling bias. The two annotators agree with each other at macro kappa = 0.62 before adjudication, below the model's agreement with either of them (0.70 and 0.69). Reaction profiles differ strongly by function: Love is about 70% of reactions per post on Educational and Promotional posts but about 33% on Skeptical-Warning, which draws the largest Haha share (about 39%).
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v1 · 2026-11-11 20:32 · 最新观测版本
AraCrypto-ML: A Multi-Label Corpus of Communicative Functions in Arabic Facebook Cryptocurrency Discourse
Arabic cryptocurrency discourse on social media is communicatively heterogeneous: a single post may report prices, explain protocols, promote investment, warn of scams, or invoke Islamic jurisprudence. Yet no annotated resource captures this functional diversity. We introduce AraCrypto-ML, a 10,000-post multi-label corpus of Arabic Facebook cryptocurrency discourse (2014-2024) from 2,898 public pages using a set of related keywords. Collected posts are annotated into six categories: Market Commentary, Educational, Promotional, Skeptical-Warning, Religious Framing, and Other/Unclear, using Fanar-2-27B under a prompt-only multi-label protocol. We validate 1,000 posts against adjudicated gold labels (macro kappa = 0.74) and apply Rogan-Gladen calibration to correct labelling bias. The two annotators agree with each other at macro kappa = 0.62 before adjudication, below the model's agreement with either of them (0.70 and 0.69). Reaction profiles differ strongly by function: Love is about 70% of reactions per post on Educational and Promotional posts but about 33% on Skeptical-Warning, which draws the largest Haha share (about 39%).
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