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Journal of Computational Social Science最新文献

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Using word embeddings to probe sentiment associations of politically loaded terms in news and opinion articles from news media outlets 使用词嵌入来探索新闻媒体新闻和评论文章中政治负载术语的情绪关联
IF 3.2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Pub Date : 2021-07-14 DOI: 10.1007/s42001-021-00130-y
David Rozado, Musa al-Gharbi
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引用次数: 11
Computational courtship understanding the evolution of online dating through large-scale data analysis 计算求爱:通过大规模数据分析了解在线约会的演变
IF 3.2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Pub Date : 2021-07-01 DOI: 10.1007/s42001-021-00132-w
Rachel Dinh, Patrick Gildersleve, Chris Blex, T. Yasseri
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引用次数: 2
OCR with Tesseract, Amazon Textract, and Google Document AI: a benchmarking experiment OCR与Tesseract、Amazon text和Google Document AI:一个基准实验
IF 3.2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Pub Date : 2021-06-24 DOI: 10.31235/osf.io/6zfvs
Thomas Hegghammer
Optical Character Recognition (OCR) can open up understudied historical documents to computational analysis, but the accuracy of OCR software varies. This article reports a benchmarking experiment comparing the performance of Tesseract, Amazon Textract, and Google Document AI on images of English and Arabic text. English-language book scans ( n  = 322) and Arabic-language article scans ( n  = 100) were replicated 43 times with different types of artificial noise for a corpus of 18,568 documents, generating 51,304 process requests. Document AI delivered the best results, and the server-based processors (Textract and Document AI) performed substantially better than Tesseract, especially on noisy documents. Accuracy for English was considerably higher than for Arabic. Specifying the relative performance of three leading OCR products and the differential effects of commonly found noise types can help scholars identify better OCR solutions for their research needs. The test materials have been preserved in the openly available “Noisy OCR Dataset” (NOD) for reuse in future benchmarking studies.
光学字符识别(OCR)可以打开未被充分研究的历史文献进行计算分析,但OCR软件的准确性存在差异。本文报告了一个基准测试实验,比较了Tesseract、Amazon text和Google Document AI在英语和阿拉伯语文本图像上的性能。英语图书扫描(n = 322)和阿拉伯语文章扫描(n = 100)使用不同类型的人工噪声对18,568个文档的语料库进行了43次复制,产生了51,304个处理请求。文档人工智能提供了最好的结果,基于服务器的处理器(文本和文档人工智能)比Tesseract表现得更好,特别是在嘈杂的文档上。英语的准确率要比阿拉伯语高得多。具体说明三种主要OCR产品的相对性能和常见噪声类型的差异影响,可以帮助学者确定更好的OCR解决方案,以满足他们的研究需求。测试材料已保存在公开可用的“噪声OCR数据集”(NOD)中,以便在未来的基准研究中重用。
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引用次数: 26
Mitigation strategies against cascading failures within a project activity network 针对项目活动网络内级联故障的缓解策略
IF 3.2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Pub Date : 2021-06-18 DOI: 10.1007/s42001-021-00123-x
C. Ellinas, C. Nicolaides, Naoki Masuda
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引用次数: 0
An analysis of US domestic migration via subset-stable measures of administrative data 通过对行政数据的子集稳定度量对美国国内移民的分析
IF 3.2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Pub Date : 2021-06-11 DOI: 10.1007/S42001-021-00124-W
B. Klemens
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引用次数: 1
Battle of positioning: exploring the role of bridges in competitive diffusion 定位之战:探讨桥梁在竞争扩散中的作用
IF 3.2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Pub Date : 2021-06-10 DOI: 10.1007/s42001-021-00127-7
Jie Gu, Yunjie Xu
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引用次数: 1
Users roles identification on online crowdsourced Q&A platforms and encyclopedias: a survey 在线众包问答平台与百科全书的用户角色识别:一项调查
IF 3.2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Pub Date : 2021-06-08 DOI: 10.1007/s42001-021-00125-9
A. Saxena, Harita Reddy
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引用次数: 6
An open software environment to make spatial access metrics more accessible 一个开放的软件环境,使空间访问度量更容易访问
IF 3.2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Pub Date : 2021-06-07 DOI: 10.1007/s42001-021-00126-8
J. Saxon, Julia Koschinsky, Karina Acosta, V. Anguiano, L. Anselin, Sergio J. Rey
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引用次数: 11
Analyzing Twitter networks using graph embeddings: an application to the British case 使用图嵌入分析Twitter网络:英国案例的应用
IF 3.2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Pub Date : 2021-06-06 DOI: 10.1007/s42001-021-00128-6
Miguel Won, Jorge M. Fernandes
{"title":"Analyzing Twitter networks using graph embeddings: an application to the British case","authors":"Miguel Won, Jorge M. Fernandes","doi":"10.1007/s42001-021-00128-6","DOIUrl":"https://doi.org/10.1007/s42001-021-00128-6","url":null,"abstract":"","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"16 1","pages":"253 - 263"},"PeriodicalIF":3.2,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78522235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Colorado in context: Congressional redistricting and competing fairness criteria in Colorado 科罗拉多州语境:科罗拉多州国会选区重划和竞争公平标准
IF 3.2 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Pub Date : 2021-05-31 DOI: 10.1007/s42001-021-00119-7
J. Clelland, H. Colgate, Daryl R. DeFord, Beth Malmskog, Flavia Sancier-Barbosa
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引用次数: 8
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