确保数字空间的安全:在社交媒体帖子中检测混合代码的仇恨言论

IF 2.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Pradeep Kumar Roy , Abhinav Kumar
{"title":"确保数字空间的安全:在社交媒体帖子中检测混合代码的仇恨言论","authors":"Pradeep Kumar Roy ,&nbsp;Abhinav Kumar","doi":"10.1016/j.datak.2025.102409","DOIUrl":null,"url":null,"abstract":"<div><div>Social networks strive to offer positive content to users, yet a considerable amount of inappropriate material, such as rumors, fake news, and hate speech, persists. Despite significant efforts to detect and prevent hate speech early, it remains widespread due to issues like misspellings and mixed language in posts. To address these challenges, this research utilizes advanced algorithms like CNN, LSTM, and BERT to develop an automated system for detecting hate speech in Telugu-English code-mixed posts. Additionally, evaluating the effectiveness of data translation and transliteration approaches for detecting hate in mixed language. Results indicate that the transliteration approach achieves the highest accuracy, with a performance of 75% accuracy, surpassing raw and translated data by 1% and 3%, respectively. The proposed system may effectively minimizes hate speech and offensive content on social media platforms, resulting in an enhanced user experience. From a managerial perspective, this research presents numerous benefits, such as improved content moderation, optimized resource allocation, data-driven decision-making, enhanced user satisfaction, strengthened reputation management, and greater scalability. These advancements underscore the potential of utilizing advanced technologies to address complex challenges in social media management.</div></div>","PeriodicalId":55184,"journal":{"name":"Data & Knowledge Engineering","volume":"156 ","pages":"Article 102409"},"PeriodicalIF":2.7000,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ensuring safety in digital spaces: Detecting code-mixed hate speech in social media posts\",\"authors\":\"Pradeep Kumar Roy ,&nbsp;Abhinav Kumar\",\"doi\":\"10.1016/j.datak.2025.102409\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Social networks strive to offer positive content to users, yet a considerable amount of inappropriate material, such as rumors, fake news, and hate speech, persists. Despite significant efforts to detect and prevent hate speech early, it remains widespread due to issues like misspellings and mixed language in posts. To address these challenges, this research utilizes advanced algorithms like CNN, LSTM, and BERT to develop an automated system for detecting hate speech in Telugu-English code-mixed posts. Additionally, evaluating the effectiveness of data translation and transliteration approaches for detecting hate in mixed language. Results indicate that the transliteration approach achieves the highest accuracy, with a performance of 75% accuracy, surpassing raw and translated data by 1% and 3%, respectively. The proposed system may effectively minimizes hate speech and offensive content on social media platforms, resulting in an enhanced user experience. From a managerial perspective, this research presents numerous benefits, such as improved content moderation, optimized resource allocation, data-driven decision-making, enhanced user satisfaction, strengthened reputation management, and greater scalability. These advancements underscore the potential of utilizing advanced technologies to address complex challenges in social media management.</div></div>\",\"PeriodicalId\":55184,\"journal\":{\"name\":\"Data & Knowledge Engineering\",\"volume\":\"156 \",\"pages\":\"Article 102409\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-01-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data & Knowledge Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0169023X25000047\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data & Knowledge Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169023X25000047","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

社交网络努力为用户提供积极的内容,然而大量不适当的材料,如谣言、假新闻和仇恨言论,仍然存在。尽管在早期发现和预防仇恨言论方面做出了重大努力,但由于拼写错误和帖子中混杂的语言等问题,仇恨言论仍然普遍存在。为了应对这些挑战,本研究利用CNN、LSTM和BERT等先进算法开发了一个自动化系统,用于检测泰卢格-英语代码混合帖子中的仇恨言论。此外,评估数据翻译和音译方法在混合语言中检测仇恨的有效性。结果表明,音译方法的准确率最高,达到75%,分别比原始数据和翻译数据高1%和3%。该系统可以有效地减少社交媒体平台上的仇恨言论和冒犯性内容,从而增强用户体验。从管理角度来看,这项研究带来了许多好处,如改进内容审核、优化资源分配、数据驱动决策、提高用户满意度、加强声誉管理和更大的可扩展性。这些进步强调了利用先进技术解决社交媒体管理中复杂挑战的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ensuring safety in digital spaces: Detecting code-mixed hate speech in social media posts
Social networks strive to offer positive content to users, yet a considerable amount of inappropriate material, such as rumors, fake news, and hate speech, persists. Despite significant efforts to detect and prevent hate speech early, it remains widespread due to issues like misspellings and mixed language in posts. To address these challenges, this research utilizes advanced algorithms like CNN, LSTM, and BERT to develop an automated system for detecting hate speech in Telugu-English code-mixed posts. Additionally, evaluating the effectiveness of data translation and transliteration approaches for detecting hate in mixed language. Results indicate that the transliteration approach achieves the highest accuracy, with a performance of 75% accuracy, surpassing raw and translated data by 1% and 3%, respectively. The proposed system may effectively minimizes hate speech and offensive content on social media platforms, resulting in an enhanced user experience. From a managerial perspective, this research presents numerous benefits, such as improved content moderation, optimized resource allocation, data-driven decision-making, enhanced user satisfaction, strengthened reputation management, and greater scalability. These advancements underscore the potential of utilizing advanced technologies to address complex challenges in social media management.
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 & Knowledge Engineering
Data & Knowledge Engineering 工程技术-计算机:人工智能
CiteScore
5.00
自引率
0.00%
发文量
66
审稿时长
6 months
期刊介绍: Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.
×
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:604180095
Book学术官方微信