从移动使用模式预测社会人口统计特征:应用程序和隐私影响
Rouzbeh Razavi1, Guisen Xue1, Ikpe Justice Akpan2
1Department of Management and Information Systems, Kent State University, Kent, Ohio, USA.
Big data
|August 15, 2023
概括
从移动使用数据中预测用户人口统计数据,如性别和年龄是可能的. 移动足迹提供了洞察力,但也引发了隐私问题,需要仔细考虑道德影响.
科学领域:
- 计算机科学 计算机科学
- 社会学 社会学 社会学
- 人与计算机的交互
背景情况:
- 移动设备产生了广泛的数字足迹.
- 这些足迹可以作为用户特征的代理,包括人口统计数据.
- 从移动使用中预测人口统计数据为个性化和市场研究提供了好处.
研究的目的:
- 从移动使用元数据中评估预测社会人口统计学属性的准确性 (年龄,性别,收入,教育).
- 量化不同人口特征的预测能力.
- 探索人口统计推断的实际应用和隐私影响.
主要方法:
- 利用了机器学习算法.
- 分析了来自235个人口统计学上不同的用户的移动使用数据.
- 对年龄,性别,收入和教育的预测准确性进行了检查.
主要成果:
- 性别预测达到最高准确度 (平衡准确度=0.862).
- 教育水平的预测更具挑战性 (平衡精度=0.719).
- 年龄和收入可以在可接受的准确度上/下分类值.
结论:
- 移动使用数据可以以不同准确度预测某些人口统计学属性.
- 调查结果强调了针对性服务和市场研究的潜在好处.
- 伦理考虑,包括隐私和歧视风险至关重要.
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