The enshittification of online search? Privacy and quality of Google, Bing and Apple in coding advice
- URL: http://arxiv.org/abs/2512.03793v1
- Date: Wed, 03 Dec 2025 13:42:22 GMT
- Title: The enshittification of online search? Privacy and quality of Google, Bing and Apple in coding advice
- Authors: Konrad Kollnig,
- Abstract summary: We evaluate the search quality of Google Search, Microsoft Bing, and Apple Search.<n>We use two independent metrics of search quality: 1) the number of trackers on the first search result, as a measure of privacy in web search, and 2) the average rank of the first Stack Overflow search result.<n>Our results suggest that the privacy of search results is higher on Bing than on Google and Apple. Similarly, the quality of coding advice -- as measured by the average rank of Stack Overflow -- was highest on Bing.
- Score: 1.8528929583956726
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Even though currently being challenged by ChatGPT and other large-language models (LLMs), Google Search remains one of the primary means for many individuals to find information on the internet. Interestingly, the way that we retrieve information on the web has hardly changed ever since Google was established in 1998, raising concerns as to Google's dominance in search and lack of competition. If the market for search was sufficiently competitive, then we should probably see a steady increase in search quality over time as well as alternative approaches to the Google's approach to search. However, hardly any research has so far looked at search quality, which is a key facet of a competitive market, especially not over time. In this report, we conducted a relatively large-scale quantitative comparison of search quality of 1,467 search queries relating to coding advice in October 2023. We focus on coding advice because the study of general search quality is difficult, with the aim of learning more about the assessment of search quality and motivating follow-up research into this important topic. We evaluate the search quality of Google Search, Microsoft Bing, and Apple Search, with a special emphasis on Apple Search, a widely used search engine that has never been explored in previous research. For the assessment of search quality, we use two independent metrics of search quality: 1) the number of trackers on the first search result, as a measure of privacy in web search, and 2) the average rank of the first Stack Overflow search result, under the assumption that Stack Overflow gives the best coding advice. Our results suggest that the privacy of search results is higher on Bing than on Google and Apple. Similarly, the quality of coding advice -- as measured by the average rank of Stack Overflow -- was highest on Bing.
Related papers
- SmartSearch: Process Reward-Guided Query Refinement for Search Agents [63.46067892354375]
Large language model (LLM)-based search agents have proven promising for addressing knowledge-intensive problems.<n>Existing works largely focus on optimizing the reasoning paradigms of search agents, yet the quality of intermediate search queries during reasoning remains overlooked.<n>We introduce SmartSearch, a framework built upon two key mechanisms to mitigate this issue.
arXiv Detail & Related papers (2026-01-08T12:39:05Z) - Implicit Search via Discrete Diffusion: A Study on Chess [104.74301574891359]
We propose DiffuSearch, a model that does textitimplicit search by looking into the future world via discrete diffusion modeling.<n>We instantiate DiffuSearch on a classical board game, Chess, where explicit search is known to be essential.<n>We show DiffuSearch outperforms both the searchless and explicit search-enhanced policies.
arXiv Detail & Related papers (2025-02-27T06:25:15Z) - Data Voids and Warning Banners on Google Search [4.4534065108405665]
We collected 1.4M unique search queries shared on social media to surface Google's warning banners.<n>We found that Google returned a warning banner for about 1% of our search queries.<n>We identify 29 to 58 times more low-quality data voids than there were low-quality banners.
arXiv Detail & Related papers (2025-02-24T18:56:04Z) - RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation [71.88883580383039]
We propose RethinkMCTS, a framework that explores and refines the reasoning process for code generation.<n>Specifically, we employ MCTS to search for thoughts before code generation and integrate MCTS with a refinement mechanism called rethink.<n>We demonstrate that RethinkMCTS outperforms previous search-based and feedback-enhanced code generation baselines.
arXiv Detail & Related papers (2024-09-15T02:07:28Z) - MindSearch: Mimicking Human Minds Elicits Deep AI Searcher [50.68599514830046]
We introduce MindSearch to mimic the human minds in web information seeking and integration.<n>The framework can be instantiated by a simple yet effective LLM-based multi-agent framework.<n> MindSearch demonstrates significant improvement in the response quality in terms of depth and breadth.
arXiv Detail & Related papers (2024-07-29T17:12:40Z) - Market or Markets? Investigating Google Search's Market Shares Under Horizontal and Vertical Segmentation [4.945772101603344]
We present the first analysis of Google Search's market share under both horizontal and vertical segmentation of online search.
We observe that Google Search receives 71.8% of participants' queries when compared to other horizontal search engines.
Our results inform the consequential and ongoing debates about the market power of Google Search and the conceptualization of online markets in general.
arXiv Detail & Related papers (2024-07-16T17:09:55Z) - Code Search Debiasing:Improve Search Results beyond Overall Ranking
Performance [10.059769537424582]
Biased code search engines provide poor user experience, even though they show promising overall performance.
We develop a general debiasing framework that employs reranking to calibrate search results.
Experiments show that our framework can effectively reduce biases.
arXiv Detail & Related papers (2023-11-25T02:31:22Z) - Tree-based Text-Vision BERT for Video Search in Baidu Video Advertising [58.09698019028931]
How to pair the video ads with the user search is the core task of Baidu video advertising.
Due to the modality gap, the query-to-video retrieval is much more challenging than traditional query-to-document retrieval.
We present a tree-based combo-attention network (TCAN) which has been recently launched in Baidu's dynamic video advertising platform.
arXiv Detail & Related papers (2022-09-19T04:49:51Z) - Exposing Query Identification for Search Transparency [69.06545074617685]
We explore the feasibility of approximate exposing query identification (EQI) as a retrieval task by reversing the role of queries and documents in two classes of search systems.
We derive an evaluation metric to measure the quality of a ranking of exposing queries, as well as conducting an empirical analysis focusing on various practical aspects of approximate EQI.
arXiv Detail & Related papers (2021-10-14T20:19:27Z) - The Matter of Chance: Auditing Web Search Results Related to the 2020
U.S. Presidential Primary Elections Across Six Search Engines [68.8204255655161]
We look at the text search results for "us elections", "donald trump", "joe biden" and "bernie sanders" queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex.
Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents.
arXiv Detail & Related papers (2021-05-03T11:18:19Z) - Search Engine Similarity Analysis: A Combined Content and Rankings
Approach [6.69087470775851]
We present an analysis of the affinity of the two major search engines, Google and Bing, along with DuckDuckGo.
We developed a new similarity metric that leverages both the content and the ranking of search responses.
We found that Google stands apart, but Bing and DuckDuckGo are largely indistinguishable from each other.
arXiv Detail & Related papers (2020-11-01T23:57:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.