从移动使用模式预测社会人口属性:应用与隐私影响

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2024-01-01 Epub Date: 2023-08-14 DOI:10.1089/big.2022.0182
Rouzbeh Razavi, Guisen Xue, Ikpe Justice Akpan
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引用次数: 0

摘要

当用户与他们的移动设备互动时,会留下独特的数字足迹,这些足迹可被视为预测性代理,揭示用户的一系列特征,包括他们的人口统计学特征。根据移动使用情况预测用户的人口统计学特征可为服务提供商和用户带来显著的好处,包括改善客户定位、服务个性化和市场研究工作。本研究利用机器学习算法和来自 235 位不同人口统计学特征用户的移动使用数据,研究了从移动使用元数据预测其社会人口属性(年龄、性别、收入和教育程度)的准确性,通过量化各属性的预测能力并讨论实际应用和隐私影响,填补了现有文献的空白。研究结果表明,从移动使用足迹中预测性别最为准确(平衡准确率 = 0.862),而预测用户的教育水平则更具挑战性(平衡准确率 = 0.719)。此外,分类模型还能根据用户的年龄或收入是否高于或低于某个阈值对其进行分类,准确率在可接受范围内。研究还介绍了从移动使用数据推断人口统计学属性的实际应用,并从不同利益相关者的角度讨论了研究结果的影响,如隐私和歧视风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Sociodemographic Attributes from Mobile Usage Patterns: Applications and Privacy Implications.

When users interact with their mobile devices, they leave behind unique digital footprints that can be viewed as predictive proxies that reveal an array of users' characteristics, including their demographics. Predicting users' demographics based on mobile usage can provide significant benefits for service providers and users, including improving customer targeting, service personalization, and market research efforts. This study uses machine learning algorithms and mobile usage data from 235 demographically diverse users to examine the accuracy of predicting their sociodemographic attributes (age, gender, income, and education) from mobile usage metadata, filling the gap in the current literature by quantifying the predictive power of each attribute and discussing the practical applications and privacy implications. According to the results, gender can be most accurately predicted (balanced accuracy = 0.862) from mobile usage footprints, whereas predicting users' education level is more challenging (balanced accuracy = 0.719). Moreover, the classification models were able to classify users based on whether their age or income was above or below a certain threshold with acceptable accuracy. The study also presents the practical applications of inferring demographic attributes from mobile usage data and discusses the implications of the findings, such as privacy and discrimination risks, from the perspectives of different stakeholders.

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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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