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$Q_{bias}$ -- A Dataset on Media Bias in Search Queries and Query Suggestions
Information Retrieval · Submitted 29 Nov 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Additionally to this work we provided datasets and language models for biasing texts that allow further research on bias in online information search.
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The abstract, sentence by sentence
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 language models 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 language models with the goal of producing models capable of biasing text with left and right political stance. Additionally to this work we provided datasets and language models for biasing texts that allow further research on bias in online information search.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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 language models for the investigation of bias in online search. | contribution 1.00 | 0.94 | 0.12 | asserted 0.99 |
| s1 | Web search engines are a major factor and trusted source in information search, especially in the political domain. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | However, biased information can influence opinion formation and lead to biased opinions. | background 1.00 | 0.05 | 0.05 | asserted 0.57 |
| s3 | To interact with search engines, users formulate search queries and interact with search query suggestions provided by the search engines. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s4 | A lack of datasets on search queries inhibits research on the subject. | gap 1.00 | 0.10 | 0.04 | asserted 1.00 |
| s5 | We use $Q_{bias}$ to evaluate different approaches to fine-tuning transformer-based language models with the goal of producing models capable of biasing text with left and right political stance. | contribution 0.52 | 0.94 | 0.18 | asserted 0.97 |
| s6 | Additionally to this work we provided datasets and language models for biasing texts that allow further research on bias in online information search. | contribution 0.86 | 0.94 | 0.20 | asserted 0.99 |
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The paper
Original 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 language models 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 language models with the goal of producing models capable of biasing text with left and right political stance. Additionally to this work we provided datasets and language models for biasing texts that allow further research on bias in online information search.
Human review
How the literature uses it
1 arXiv paper cite this in 1 sentence
Papers citing it
Uses its data or values 1 paper
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We use the QBias corpus [ref] , which contains 21,747 U.S. political news articles collected from AllSides balanced news headline roundups (June 2012–November 2022) and labeled by expert annotators as left, center, or right.
Directional Hallucinations: Ideological Drift in News-Grounded LLM Question Answering · Data and Task · 0.98
What it cites
Uses the method or tool of 1 paper
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With the goal of capturing biased language in pretrained language models and using the HuggingFace Transformers module [ref] , we fine-tune the base DistilBERT model, which was trained in Wikipedia articles and a large book dataset.[<https://huggingface.co/distilbert-base-uncased>] DistilBERT– [ref] was chosen as a base model since it has proven to perform well with comparably small datasets, performs better than BERT despite its smaller size [ref] , and for its aptness for adopting domain-specificity [ref] .
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter · Domain-Adopting DistilBERT · 0.97
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