Investigating Bias in Political Search Query Suggestions by Relative Comparison with LLMs
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916463207514112 |
|---|---|
| author | Haak, Fabian Engelmann, Björn Kreutz, Christin Katharina Schaer, Philipp |
| author_facet | Haak, Fabian Engelmann, Björn Kreutz, Christin Katharina Schaer, Philipp |
| contents | Search query suggestions affect users' interactions with search engines, which then influences the information they encounter. Thus, bias in search query suggestions can lead to exposure to biased search results and can impact opinion formation. This is especially critical in the political domain. Detecting and quantifying bias in web search engines is difficult due to its topic dependency, complexity, and subjectivity. The lack of context and phrasality of query suggestions emphasizes this problem. In a multi-step approach, we combine the benefits of large language models, pairwise comparison, and Elo-based scoring to identify and quantify bias in English search query suggestions. We apply our approach to the U.S. political news domain and compare bias in Google and Bing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23879 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Investigating Bias in Political Search Query Suggestions by Relative Comparison with LLMs Haak, Fabian Engelmann, Björn Kreutz, Christin Katharina Schaer, Philipp Information Retrieval 94-02 H.3.3 Search query suggestions affect users' interactions with search engines, which then influences the information they encounter. Thus, bias in search query suggestions can lead to exposure to biased search results and can impact opinion formation. This is especially critical in the political domain. Detecting and quantifying bias in web search engines is difficult due to its topic dependency, complexity, and subjectivity. The lack of context and phrasality of query suggestions emphasizes this problem. In a multi-step approach, we combine the benefits of large language models, pairwise comparison, and Elo-based scoring to identify and quantify bias in English search query suggestions. We apply our approach to the U.S. political news domain and compare bias in Google and Bing. |
| title | Investigating Bias in Political Search Query Suggestions by Relative Comparison with LLMs |
| topic | Information Retrieval 94-02 H.3.3 |
| url | https://arxiv.org/abs/2410.23879 |