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SaTC: CORE: Medium: Collaborative: A Linguistically-Informed Approach for Measuring and Circumventing Internet Censorship

SaTC：核心：媒介：协作：衡量和规避互联网审查的语言知情方法

基本信息

批准号：
1704113
负责人：
Chris Leberknight
金额：
$ 50.08万
依托单位：
Montclair State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-15 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1704113&HistoricalAwards=false
关键词：
SaTC CORE Medium Collaborative Linguistically

项目摘要

Internet censorship consists of restrictions on what information can be publicized or viewed on the Internet. According to Freedom House's annual Freedom on the Net report, more than half the world's Internet users now live in a place where the Internet is censored or restricted. However, members of the Internet Freedom community lack comprehensive real-time awareness of where and how censorship is being imposed. The challenges to achieving such a solution include but are not limited to coverage, scalability, adoption, and safety. The project explores a linguistically-informed approach for measuring and circumventing Internet censorship.The research takes a new perspective on the problem by investigating a hybrid method for censorship detection and evasion from the lens of linguistic analysis. The team develops new models to measure Internet censorship, investigates mechanisms to circumvent censorship using linguistic techniques, conducts communication and social network measurements of censored content. Active Sensing and natural language processing techniques, in conjunction with machine learning and optimization, invigorates new research directions in Internet Freedom and produces new high quality data and tools available for public use. This new allogamy between computer science, information security, network analysis and linguistics provides the foundation for evolution of anti-censorship technologies. The research contributes to a number of fields including Internet censorship, privacy and online information retrieval, as well as computational social science by modeling and analyzing the phenomenon of censorship using the signal available in language. The broader contribution includes wide dissemination of the research results via peer-reviewed publications, special topic courses and workshops. Additional benefits include providing graduate and undergraduate researchers with significant experience of highly practical work on a difficult interdisciplinary problem. Significant gains are obtained in recruitment of minority students through research training in computer science and linguistics.

互联网审查包括限制哪些信息可以在互联网上公布或查看。根据自由之家的年度网络自由报告，世界上一半以上的互联网用户现在生活在互联网受到审查或限制的地方。然而，互联网自由社区的成员缺乏对审查在何处以及如何实施的全面实时意识。实现这种解决方案的挑战包括但不限于覆盖范围，可扩展性，采用和安全性。该项目探讨了一种基于语言学的方法来衡量和规避互联网审查。该研究从语言分析的透镜出发，研究了一种检测和规避审查的混合方法，从而从一个新的角度来看待这个问题。该团队开发新的模型来衡量互联网审查，调查使用语言技术规避审查的机制，对审查内容进行通信和社交网络测量。主动传感和自然语言处理技术，结合机器学习和优化，为互联网自由提供了新的研究方向，并产生了新的高质量数据和工具供公众使用。计算机科学、信息安全、网络分析和语言学之间的这种新的关系为反审查技术的发展提供了基础。该研究有助于许多领域，包括互联网审查，隐私和在线信息检索，以及计算社会科学的建模和分析审查现象使用的信号在语言中。更广泛的贡献包括通过同行评审的出版物、专题课程和讲习班广泛传播研究成果。额外的好处包括提供研究生和本科生研究人员在一个困难的跨学科问题的高度实际工作的显着经验。通过计算机科学和语言学的研究培训，在招收少数民族学生方面取得了重大进展。

项目成果

期刊论文数量（21）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms

搜索 PET：使用分布和基于情感的方法查找潜在的委婉术语

DOI：
发表时间：
2022
期刊：
USA
影响因子：
0
作者：
Gavidia, M.
通讯作者：
Gavidia, M.

NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification

DOI：
10.48550/arxiv.2305.10971
发表时间：
2023-05
期刊：
ArXiv
影响因子：
0
作者：
Iyanuoluwa Shode;David Ifeoluwa Adelani;J. Peng;Anna Feldman
通讯作者：
Iyanuoluwa Shode;David Ifeoluwa Adelani;J. Peng;Anna Feldman

Proceedings of the First Workshop on Natural Language Processing for Internet Freedom (NLP4IF) Workshop: Censorship, Disinformation, and Propaganda.

第一届自然语言处理促进互联网自由研讨会 (NLP4IF) 研讨会论文集：审查制度、虚假信息和宣传。

DOI：
发表时间：
2018
期刊：
and Propaganda.
影响因子：
0
作者：
Feldman, A.;Da San Martino, G;Leberknight, C.;Nakov, P.
通讯作者：
Nakov, P.

Neural Network Prediction of Censorable Language

可审查语言的神经网络预测

DOI：
发表时间：
2019
期刊：
Proceedings of the 3rd Workshop on NLP and Computational Social Science (NLP+CSS
影响因子：
0
作者：
Ng Kei Y;Feldman A;Peng J.,
通讯作者：
Peng J.,

You Don’t Say....Linguistic Features in Sarcasm Detection

你不说......讽刺检测中的语言特征

DOI：
发表时间：
2021
期刊：
CLIC-IT 2021: Seventh Italian Conference on Computational Linguistics Bologna
影响因子：
0
作者：
Ducret M.;Kruse L.;Martinez C.;Feldman A.;Peng, J.
通讯作者：
Peng, J.