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基于网络内容的统计:未来的挑战.pdf

上传人: Fl****zo 编号:718604 2025-06-22 10页 168.43KB

1、31/01/2025Web Content-Based Statistics:The Challenges AheadFernando REISWeb Intelligence Network Conference-From Web to Data Gdansk,4-5 February 2025Challenges OverviewInstability of the WebDuplication of objectsAutomatic information extractionFakery and misinformationRepresentativenessInstability o

2、f the WebWebsites appear,disappear,or changeDowntime and access restrictionsImpact on continuity and time series consistencyIts unavoidableWe need methods to address this instabilityE.g.Chaining Promissing,but we need to address breakdownsDuplication of ObjectsA curse and a blessingDuplicates lead t

3、o over-estimation of totalsRedundancy across websites,reduces impact of instability of the webDuplication happens across websites and within websitesPossible solutions:Restrict the web sources:eliminates the curse,but also the blessingIncrease the effectiveness of the deduplicationSurveys on web sou

4、rces owners and statistical units(enterprises,individuals)Automatic Information ExtractionNeed for automated methods(NLP,AI)Human annotation/labelling is very expensivePrecision of latest AI developments(LLM)put algorithms at par with humansTrade-off between cost and precision of AIMeasurement error

5、s introduced by algorithms bias our statisticsWe must be able to measure the precision of the algorithmsSolution(s):We urgently need gold standards/test datasets to estimate precision using LLMsFakery and misinformationHow fakery differs from noise biasIntentional distortions targeting key variables

6、Not much work done in official statisticsSolutions:Source validation and trustworthiness assessmentDetection using AICross-validation with other data sourcesHuman expert oversight&hybrid approachesRepresentativenessCoverage and selectivityBias in web-based dat

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本文主要讨论网络内容为基础的统计学面临的挑战。关键点如下: 1. 网络不稳定性:网站出现、消失或更改,导致数据连续性和时间序列一致性受影响。需发展方法应对,如链接技术。 2. 对象重复:重复数据既带来过估计问题,也因网站间的冗余减轻了网络不稳定性的影响。解决方案包括限制数据源和提升去重效果。 3. 自动信息提取:依赖自动化方法(如自然语言处理和人工智能),但算法引入的测量误差会影响统计数据准确性。需制定黄金标准/测试数据集以评估算法精确度。 4. 伪造和误信息:有意扭曲关键变量,与噪声不同,目前官方统计研究不足。解决方法包括来源验证、AI检测和交叉验证。 5. 代表性:网络数据的覆盖和选择性导致偏差。需采用纠正选择性的估计方法。 文章强调跨学科合作和基础设施及专业知识投资对未来发展至关重要。
"网页不稳定性如何影响统计?" "如何利用AI应对网络虚假信息?" "网络数据代表性面临哪些挑战?"
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