arXiv cs.CL· F\'abio A. S. Passos, P\'ericles B. C. Miranda, Ana Paula Cavalcanti·· 10 小时前AI 评分26
MISO-BR：巴西葡萄牙语厌女内容检测开放语料库
MISO-BR: An Open Corpus for Misogyny Detection in Brazilian Portuguese
AI 导读
MISO-BR 是一个开放、有文档和版本管理的巴西葡萄牙语厌女检测语料库，含 896 条均衡社交媒体文本，并公开构建协议与可复现基线。语料结合 X 平台的词典与目标引导抓取、OLID-BR 导入条目及大语言模型初筛，三名标注者在 816 条上达到 Fleiss' kappa 0.788。
正文 · 原文
Abstract:Misogyny is among the most prevalent forms of online hate speech directed at Brazilian users, yet open and auditable resources dedicated to it in Brazilian Portuguese remain scarce. We introduce MISO-BR, an open, documented and versioned corpus of 896 balanced social media texts annotated for binary misogyny, released with its construction protocol and reproducible baselines. The corpus combines lexicon- and target-guided scraping from X with items imported from OLID-BR, a large language model triage stage whose prompt and decoding parameters are released, and manual annotation by three annotators following a written guide. Inter-annotator reliability reaches a Fleiss' kappa of 0.788 over 816 items. Under a single fixed protocol we compare four classical classifiers (Multinomial and Bernoulli Naive Bayes, a linear SVM and a random forest) with fine-tuned BERTimbau Base and Large. The classical models cluster between 0.651 and 0.676 macro-F1 and are precision-oriented but recall-poor. The transformers reach 0.833 macro-F1 and accuracy (Wilson 95% CI for accuracy [0.77, 0.88]), a gap of roughly 16 points, while Base and Large are indistinguishable on the test set despite a 2.6x difference in training cost. We argue that the value of the resource lies less in its headline scores than in the auditability of its pipeline, and we state three limits that users must reckon with: the artificial class balance, whose effect on precision under realistic prevalence we quantify; the association between provenance and label, which the released source field makes measurable; and the selection bias induced by lexicon-guided sampling and automatic triage. The corpus, annotation guide, models and experimental scripts are publicly available.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.14036 [cs.CL] |
| (or arXiv:2611.14036v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14036 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Pericles Miranda Dr [view email]
[v1]
Tue, 10 Nov 2026 13:03:50 UTC (24 KB)
来源：arXiv cs.CL · arxiv.org