Recognition of Hate or Offensive Tweets in the Online Communities

K. Machová, D. Suchanic, V. Maslej-Krešňáková
{"title":"Recognition of Hate or Offensive Tweets in the Online Communities","authors":"K. Machová, D. Suchanic, V. Maslej-Krešňáková","doi":"10.1109/ICETA51985.2020.9379227","DOIUrl":null,"url":null,"abstract":"The paper focuses on classification of text into categories as hate speech or offensive language which represent unhealthy phenomena complicating learning and communication in online space. This classification was achieved by training a model using a deep neural network. The network was tested with different amounts of neurons in the hidden layer, with three distinctive optimizers and with various learning rates.","PeriodicalId":149716,"journal":{"name":"2020 18th International Conference on Emerging eLearning Technologies and Applications (ICETA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 18th International Conference on Emerging eLearning Technologies and Applications (ICETA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICETA51985.2020.9379227","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

The paper focuses on classification of text into categories as hate speech or offensive language which represent unhealthy phenomena complicating learning and communication in online space. This classification was achieved by training a model using a deep neural network. The network was tested with different amounts of neurons in the hidden layer, with three distinctive optimizers and with various learning rates.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
网络社区中仇恨或攻击性推文的识别
本文的重点是将文本分类为仇恨言论或攻击性语言,它们代表了网络空间中使学习和交流复杂化的不健康现象。这种分类是通过使用深度神经网络训练模型来实现的。用隐藏层中不同数量的神经元、三种不同的优化器和不同的学习率对网络进行了测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Data Science and its position in university education within National Project IT Academy-Education for 21st Century Massification of Online Education: A Holistic Strategy Speech Emotion Recognition Overview and Experimental Results HIP-Based Security in IoT Networks: A comparison On-Chip Digital Calibration for Analog ICs Towards Improved Reliability in Nanotechnologies
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1