A Deep Decision Forests Model for Hate Speech Detection

IF 0.9 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Jordanian Journal of Computers and Information Technology Pub Date : 2023-01-01 DOI:10.5455/jjcit.71-1667394363
M. Ndenga
{"title":"A Deep Decision Forests Model for Hate Speech Detection","authors":"M. Ndenga","doi":"10.5455/jjcit.71-1667394363","DOIUrl":null,"url":null,"abstract":"Detecting and controlling propagation of hate-speech over social media platforms is a challenge. This problem is exacerbated by extreme fast flow, readily available audience, and relative permanence of information on social media. The objective of this research is to propose a model that could be used to detect political hate speech that is propagated through social media platforms in Kenya. Using Twitter textual data and Keras TensorFlow Decision Forests (TF-DF), three models were developed i.e., Gradient Boosted Trees with Universal Sentence Embeddings(USE), Gradient Boosted Trees, and Random Forest respectively. The Gradient Boosted Trees with USE model exhibited a superior performance with an accuracy of 98.86%, recall of 0.9587, precision of 0.9831, and AUC of 0.9984. Therefore, this model can be utilized for detecting hate speech on social media platforms.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jordanian Journal of Computers and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5455/jjcit.71-1667394363","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Detecting and controlling propagation of hate-speech over social media platforms is a challenge. This problem is exacerbated by extreme fast flow, readily available audience, and relative permanence of information on social media. The objective of this research is to propose a model that could be used to detect political hate speech that is propagated through social media platforms in Kenya. Using Twitter textual data and Keras TensorFlow Decision Forests (TF-DF), three models were developed i.e., Gradient Boosted Trees with Universal Sentence Embeddings(USE), Gradient Boosted Trees, and Random Forest respectively. The Gradient Boosted Trees with USE model exhibited a superior performance with an accuracy of 98.86%, recall of 0.9587, precision of 0.9831, and AUC of 0.9984. Therefore, this model can be utilized for detecting hate speech on social media platforms.
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
仇恨语音检测的深度决策森林模型
检测和控制社交媒体平台上仇恨言论的传播是一项挑战。社交媒体上的信息流动极快、容易获得的受众和相对永久性加剧了这一问题。本研究的目的是提出一个模型,可用于检测通过肯尼亚社交媒体平台传播的政治仇恨言论。利用Twitter文本数据和Keras TensorFlow决策森林(TF-DF),分别开发了具有通用句子嵌入的梯度增强树(USE)、梯度增强树和随机森林三种模型。使用USE模型的梯度增强树的准确率为98.86%,召回率为0.9587,精密度为0.9831,AUC为0.9984。因此,该模型可以用于检测社交媒体平台上的仇恨言论。
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
Jordanian Journal of Computers and Information Technology
Jordanian Journal of Computers and Information Technology Computer Science-Computer Science (all)
CiteScore
3.10
自引率
25.00%
发文量
19
期刊最新文献
OPTIMAL ENERGY CONSUMPTION AND COST PERFORMANCE SOLUTION WITH DELAY CONSTRAINTS ON FOG COMPUTING ORTHOGONAL REGRESSED STEEPEST DESCENT DEEP PERCEPTIVE NEURAL LEARNING FOR IoT- AWARE SECURED BIG DATA COMMUNICATION AUTOMATIC DETECTION OF PNEUMONIA USING CONCATENATED CONVOLUTIONAL NEURAL NETWORK DESIGN OF A COMPACT BROADBAND ANTENNA USING CHARACTERISTIC MODE ANALYSIS FOR MICROWAVE APPLICATIONS Effectiveness of zero-shot models in automatic Arabic Poem generation
×
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