作者归属与时间数据在Reddit

Guilherme Ramos Casimiro, L. A. Digiampietri
{"title":"作者归属与时间数据在Reddit","authors":"Guilherme Ramos Casimiro, L. A. Digiampietri","doi":"10.1145/3535511.3535515","DOIUrl":null,"url":null,"abstract":"Context: The practicality brought by the use of smartphones has resulted, in recent years, in greater interaction through online social networks. Problem: Social networks can influence users both positively and negatively, one of the negative impacts is the spread of fake news. In this context, identifying the correct source of information or whether the information is true becomes an extremely relevant activity. Solution: This paper presents an approach for authorship attributions that combines text mining and temporal analysis techniques. IS Theory: This work is under the Social Network Theory, in particular, the user interaction through a forum network model, in which each post creates a comment thread and the user can reply or not inside the thread. Method: This work is a controlled experiment and it aims to extend a previous case study that used a classification between two and ten authors. The results were validated through a quantitative approach. Summary of Results: Among 10 authors, classification results had more than 97% of accuracy with chars feature having more than 99% of accuracy, among 100 authors all features presented more than 70% of accuracy. Contributions and Impact in the IS area: The main contribution of this works is to validate the authorship attribution in a big data context, using significant features and a robust classifier model.","PeriodicalId":106528,"journal":{"name":"Proceedings of the XVIII Brazilian Symposium on Information Systems","volume":"142 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Authorship Attribution with Temporal Data in Reddit\",\"authors\":\"Guilherme Ramos Casimiro, L. A. Digiampietri\",\"doi\":\"10.1145/3535511.3535515\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Context: The practicality brought by the use of smartphones has resulted, in recent years, in greater interaction through online social networks. Problem: Social networks can influence users both positively and negatively, one of the negative impacts is the spread of fake news. In this context, identifying the correct source of information or whether the information is true becomes an extremely relevant activity. Solution: This paper presents an approach for authorship attributions that combines text mining and temporal analysis techniques. IS Theory: This work is under the Social Network Theory, in particular, the user interaction through a forum network model, in which each post creates a comment thread and the user can reply or not inside the thread. Method: This work is a controlled experiment and it aims to extend a previous case study that used a classification between two and ten authors. The results were validated through a quantitative approach. Summary of Results: Among 10 authors, classification results had more than 97% of accuracy with chars feature having more than 99% of accuracy, among 100 authors all features presented more than 70% of accuracy. Contributions and Impact in the IS area: The main contribution of this works is to validate the authorship attribution in a big data context, using significant features and a robust classifier model.\",\"PeriodicalId\":106528,\"journal\":{\"name\":\"Proceedings of the XVIII Brazilian Symposium on Information Systems\",\"volume\":\"142 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the XVIII Brazilian Symposium on Information Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3535511.3535515\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the XVIII Brazilian Symposium on Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3535511.3535515","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

背景:近年来,智能手机的使用带来的实用性使得人们通过在线社交网络进行了更多的互动。问题:社交网络可以对用户产生积极和消极的影响,其中一个负面影响是假新闻的传播。在这种情况下,确定信息的正确来源或信息是否真实成为一项极其相关的活动。解决方案:本文提出了一种结合文本挖掘和时间分析技术的作者归属方法。IS理论:这项工作是在社会网络理论下进行的,特别是通过论坛网络模型进行用户交互,其中每个帖子创建一个评论线程,用户可以在线程内回复或不回复。方法:这项工作是一个对照实验,它的目的是扩展以前的案例研究,使用两个和十个作者之间的分类。通过定量方法验证了结果。结果总结:10位作者分类结果准确率在97%以上,其中字符特征准确率在99%以上,100位作者分类结果准确率均在70%以上。在信息系统领域的贡献和影响:这项工作的主要贡献是在大数据背景下验证作者归属,使用重要特征和鲁棒分类器模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Authorship Attribution with Temporal Data in Reddit
Context: The practicality brought by the use of smartphones has resulted, in recent years, in greater interaction through online social networks. Problem: Social networks can influence users both positively and negatively, one of the negative impacts is the spread of fake news. In this context, identifying the correct source of information or whether the information is true becomes an extremely relevant activity. Solution: This paper presents an approach for authorship attributions that combines text mining and temporal analysis techniques. IS Theory: This work is under the Social Network Theory, in particular, the user interaction through a forum network model, in which each post creates a comment thread and the user can reply or not inside the thread. Method: This work is a controlled experiment and it aims to extend a previous case study that used a classification between two and ten authors. The results were validated through a quantitative approach. Summary of Results: Among 10 authors, classification results had more than 97% of accuracy with chars feature having more than 99% of accuracy, among 100 authors all features presented more than 70% of accuracy. Contributions and Impact in the IS area: The main contribution of this works is to validate the authorship attribution in a big data context, using significant features and a robust classifier model.
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信