附加注释的沃洛夫语-法语混合语推文数据用于检测厌恶信息

IF 1 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-03-23 DOI:10.1016/j.dib.2025.111500

Ibrahima Ndao , Khadim Dramé , Gorgoumack Sambe , Gayo Diallo

{"title":"附加注释的沃洛夫语-法语混合语推文数据用于检测厌恶信息","authors":"Ibrahima Ndao , Khadim Dramé , Gorgoumack Sambe , Gayo Diallo","doi":"10.1016/j.dib.2025.111500","DOIUrl":null,"url":null,"abstract":"<div><div>Automatic detection of obnoxious (abusive) messages on social networks is complex, especially for low-resource languages and in the case of mixed code, such as Wolof-French. This phenomenon is common in Senegalese tweets, but there is a lack of annotated data to facilitate this task. To fill this gap, we created AWOFRO, the first annotated corpus of 3510 tweets in mixed code. We analysed this corpus and validated the annotations using measures such as Cohen's Kappa.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111500"},"PeriodicalIF":1.0000,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Annotated tweet data of mixed Wolof-French for detecting Obnoxious messages\",\"authors\":\"Ibrahima Ndao , Khadim Dramé , Gorgoumack Sambe , Gayo Diallo\",\"doi\":\"10.1016/j.dib.2025.111500\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Automatic detection of obnoxious (abusive) messages on social networks is complex, especially for low-resource languages and in the case of mixed code, such as Wolof-French. This phenomenon is common in Senegalese tweets, but there is a lack of annotated data to facilitate this task. To fill this gap, we created AWOFRO, the first annotated corpus of 3510 tweets in mixed code. We analysed this corpus and validated the annotations using measures such as Cohen's Kappa.</div></div>\",\"PeriodicalId\":10973,\"journal\":{\"name\":\"Data in Brief\",\"volume\":\"60 \",\"pages\":\"Article 111500\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2025-03-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data in Brief\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S235234092500232X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S235234092500232X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

摘要

自动检测社交网络上令人讨厌的（辱骂的）消息是很复杂的，特别是对于低资源语言和混合代码的情况，如Wolof-French。这种现象在塞内加尔的推文中很常见，但缺乏注释数据来促进这项任务。为了填补这一空白，我们创建了AWOFRO，这是混合代码中3510条tweet的第一个带注释的语料库。我们分析了这个语料库，并使用Cohen’s Kappa等方法验证了注释。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Annotated tweet data of mixed Wolof-French for detecting Obnoxious messages

Automatic detection of obnoxious (abusive) messages on social networks is complex, especially for low-resource languages and in the case of mixed code, such as Wolof-French. This phenomenon is common in Senegalese tweets, but there is a lack of annotated data to facilitate this task. To fill this gap, we created AWOFRO, the first annotated corpus of 3510 tweets in mixed code. We analysed this corpus and validated the annotations using measures such as Cohen's Kappa.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.