$Q_{bias}$ -- A Dataset on Media Bias in Search Queries and Query Suggestions (2311.17780v1)

Abstract: This publication describes the motivation and generation of $Q_{bias}$, a large dataset of Google and Bing search queries, a scraping tool and dataset for biased news articles, as well as LLMs for the investigation of bias in online search. Web search engines are a major factor and trusted source in information search, especially in the political domain. However, biased information can influence opinion formation and lead to biased opinions. To interact with search engines, users formulate search queries and interact with search query suggestions provided by the search engines. A lack of datasets on search queries inhibits research on the subject. We use $Q_{bias}$ to evaluate different approaches to fine-tuning transformer-based LLMs with the goal of producing models capable of biasing text with left and right political stance. Additionally to this work we provided datasets and LLMs for biasing texts that allow further research on bias in online information search.