理论研究

网络行为数据的适用性评估问题初探

  • 聂磊 ,
  • 王延飞
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  • 北京大学信息管理系 北京 100871
聂磊(ORCID:0000-0003-1995-4114),博士研究生。

收稿日期: 2018-08-30

  修回日期: 2018-11-05

  网络出版日期: 2019-03-20

基金资助

本文系国家社会科学基金重大项目"情报学学科建设与情报工作未来发展路经研究"(项目编号:17ZDA291)研究成果之一。

A Preliminary Study About the Adequacy Evaluation of Internet Behavior Data

  • Nie Lei ,
  • Wang Yanfei
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  • Department of Information Management, Peking University, Beijing 100871

Received date: 2018-08-30

  Revised date: 2018-11-05

  Online published: 2019-03-20

摘要

[目的/意义]探讨网络行为数据适用性的意涵、影响因素和评估方法,为相关研究提供参考,以促进此类数据的科学使用。[方法/过程]利用文献法梳理出网络行为数据适用性的核心影响因素,进而以此为基础,结合情报素材评估和社会调查数据评估领域的已有成果,探索如何对网络行为数据的适用性进行评估。[结果/结论]最终提出符合网络行为数据特征的适用性评估框架与方法,并结合案例初步验证所提方法的可用性。

本文引用格式

聂磊 , 王延飞 . 网络行为数据的适用性评估问题初探[J]. 图书情报工作, 2019 , 63(6) : 29 -34 . DOI: 10.13266/j.issn.0252-3116.2019.06.004

Abstract

[Purpose/significance] This paper explores the meaning, influencing factors and assessment methods of the adequacy of Internet behavior data,in order to provide reference for related research and promote the scientific use of such data. [Method/process] This paper firstly uses the literature method to sort out the core influencing factors of the adequacy of Internet behavior data, and then based on this, uses the existing results in the field of information material evaluation and social survey data evaluation to explore how to evaluate the adequacy of Internet behavior data. [Result/conclusion] Finally, the evaluation framework and method of Internet behavior data are proposed, and the usability of the proposed method has been verified by research cases.

参考文献

[1] 金,基欧汉,维巴. 社会科学中的研究设计[M]. 陈硕,译. 上海:格致出版社, 2014.
[2] GOLDER S A,MACY M W. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures[J]. Science, 2011, 333(6051):1878-1881.
[3] 王延飞,赵柯然,陈美华,等. 情报感知的研究解析[J]. 情报理论与实践, 2018, 41(8):1-4.
[4] WILLIAMS H J,BLUM I. Defining second generation open source intelligence (OSINT) for the defense enterprise[EB/OL].[2018-06-20]. https://www.rand.org/pubs/research_reports/RR1964.html.
[5] 巴比. 社会研究方法[M]. 邱泽奇,译.11版.北京:华夏出版社, 2009.
[6] POWER D J. Using‘big data’ for analytics and decision support[J]. Journal of decision systems, 2014, 23(2):222-228.
[7] LAZER D,KENNEDY R,KING G, et al. The parable of google flu:traps in big data analysis[J]. Science, 2014, 343(6176):1203-1205.
[8] KAISLER S,ARMOUR F,ESPINOSA J A, et al. Big data:issues and challenges moving forward[C]//Proceedings of 201346th Hawaii international conference on system sciences. HI:IEEE, 2013:995-1004.
[9] BOYD D,CRAWFORD K. Critical questions for big data[J]. Information communication & society, 2012, 15(5):662-679.
[10] MERINO J,CABALLERO I,RIVAS B, et al. A data quality in use model for big data[J]. Future generation computer systems, 2016, 63(C):123-130.
[11] UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE BIG DATA QUALITY TASK TEAM. A suggested framework for the quality of big data[EB/OL].[2018-05-21]. http://www1.unece.org/stat/platform/download/attachments/108102944/Big%20Data%20Quality%20Framework%20-%20final-%20Jan08-2015.pdf?version=1&modificationDate=1420725063663&api=v2.
[12] LIU J,LI J,LI W, et al. Rethinking big data:a review on the data quality and usage issues[J]. Isprs journal of photogrammetry and remote sensing, 2016, 115(C):134-142.
[13] TUFEKCI Z. Big questions for social media big data:representativeness, validity and other methodological pitfalls[C]//Proceedings of the eighth international AAAI conference on weblogs and social media.MI:AAAI,2014.
[14] 黄恒君,陶然,傅德印. 单位名录库更新:互联网大数据源及其数据质量评估[J]. 统计研究, 2017, 34(1):12-22.
[15] 王延飞,秦铁辉. 信息分析与决策[M]. 2版.北京:北京大学出版社, 2010.
[16] 周军. 论社会科学情报素材鉴别[J]. 情报资料工作, 2005(3):46-48.
[17] 袁方. 社会研究方法教程[M]. 北京:北京大学出版社, 2004.
[18] GROVES R M. Three eras of survey research[J]. Public opinion quarterly, 2011, 75(5):861-871.
[19] 新浪科技. 新浪发布2017年第四季度及全年未经审计财报[EB/OL].[2018-05-21]. http://tech.sina.com.cn/i/2018-02-13/doc-ifyrpeie3108640.shtml.
[20] GARTNER INC. What is big data?[EB/OL].[2018-05-21]. https://www.gartner.com/it-glossary/big-data.
[21] 中国互联网络信息中心. 第41次中国互联网络发展状况统计报告[EB/OL].[2018-05-21]. http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201803/P020180305409870339136.pdf.
[22] 中华人民共和国国家统计局. 中华人民共和国2017年国民经济和社会发展统计公报[EB/OL].[2018-05-28]. http://www.stats.gov.cn/tjsj/zxfb/201802/t20180228_1585631.html.
[23] LOTAN G. Data reveals that "occupying" twitter trending topics is harder than it looks[EB/OL].[2018-05-21]. http://blog.socialflow.com/post/7120244374/data-reveals-that-occupyingtwitter-trending-topics-is-harder-than-it-looks.
[24] DUGGAN M,BRENNER J. The demographics of social media users-2012[EB/OL].[2018-05-21]. http://www.pewinternet.org/2013/02/14/the-demographics-of-social-media-users-2012.
[25] DOYLE G. Mapping dialectal variation by querying social media[C]//Proceedings of the 14th conference of the European chapter of the Association for Computational Linguistics. Gothenburg:Association for Computational Linguistics, 2014:98-106.
[26] BRODERSEN K H,GALLUSSER F,KOEHLER J, et al. Inferring causal impact using bayesian structural time-series models[J]. Annals of applied statistics, 2015, 9(1):247-274.
[27] 孙毅,吕本富,陈航,等. 基于网络搜索行为的消费者信心指数构建及应用研究[J]. 管理评论, 2014, 26(10):117-125.
[28] SCOTT S L,VARIAN H R. Predicting the present with bayesian structural time series[J]. Social science electronic publishing, 2012, 5(1):4-23.
[29] 孙毅,吕本富,陈航,等. 大数据视角的通胀预期测度与应用研究[J]. 管理世界, 2014(4):171-172.
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