通过递归分析搜索建议，发现政治人物搜索中的主题偏见
Auditing Search Query Suggestion Bias Through Recursive Algorithm Interrogation
- 用递归查询构建建议树，挖掘深层隐藏的搜索建议
- 在政治人物搜索中发现显著的主题群体偏见
- 适合研究搜索引擎公平性与信息生态的学者
尽管搜索建议在在线信息检索中扮演重要角色，但其研究仍远不如搜索引擎其他方面深入。主要障碍在于上下文稀疏和每条查询仅提供最多十个建议，导致难以识别偏见。现有最有效方法是利用同一查询随时间产生的后续建议来增强数据基础。本文提出一种新方法，通过递归算法探查技术构建建议树，获取更多隐性搜索建议，从而深化偏见分析的数据基础。基于此，我们探究了政治领域中人物相关搜索的主题群体偏见。
原文摘要 · Abstract (English)
Despite their important role in online information search, search query suggestions have not been researched as much as most other aspects of search engines. Although reasons for this are multi-faceted, the sparseness of context and the limited data basis of up to ten suggestions per search query pose the most significant problem in identifying bias in search query suggestions. The most proven method to reduce sparseness and improve the validity of bias identification of search query suggestions so far is to consider suggestions from subsequent searches over time for the same query. This work presents a new, alternative approach to search query bias identification that includes less high-level suggestions to deepen the data basis of bias analyses. We employ recursive algorithm interrogation techniques and create suggestion trees that enable access to more subliminal search query suggestions. Based on these suggestions, we investigate topical group bias in person-related searches in the political domain.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。