Many users of web search engines have been complaining in recent years about the supposedly decreasing quality of search results. This is often attributed to an increasing amount of search-engine-optimized but low-quality content. Evidence for this has always been anecdotal, yet it’s not unreasonable to think that popular online marketing strategies such as affiliate marketing incentivize the mass production of such content to maximize clicks. Since neither this complaint nor affiliate marketing as such have received much attention from the IR community, we hereby lay the groundwork by conducting an in-depth exploratory study of how affiliate content affects today’s search engines. We monitored Google, Bing and DuckDuckGo for a year on 7,392 product review queries. Our findings suggest that all search engines have significant problems with highly optimized (affiliate) content—more than is representative for the entire web according to a baseline retrieval system on the ClueWeb22. Focussing on the product review genre, we find that only a small portion of product reviews on the web uses affiliate marketing, but the majority of all search results do. Of all affiliate networks, Amazon Associates is by far the most popular. We further observe an inverse relationship between affiliate marketing use and content complexity, and that all search engines fall victim to large-scale affiliate link spam campaigns. However, we also notice that the line between benign content and spam in the form of content and link farms becomes increasingly blurry—a situation that will surely worsen in the wake of generative AI. We conclude that dynamic adversarial spam in the form of low-quality, mass-produced commercial content deserves more attention. (Code and data: https://github.com/webis-de/ECIR-24).
Is Google Getting Worse? A Longitudinal Investigation of SEO Spam in Search Engines
Many users of web search engines have been complaining in recent years about the supposedly decreasing quality of search results. This is often attributed to an increasing amount of search-engine-optimized but low-quality content. Evidence for this has always been anecdotal, yet it’s not unreasonable to think that popular online marketing strategies such as affiliate marketing incentivize the mass production of such content to maximize clicks. Since neither this complaint nor affiliate marketing as such have received much attention from the IR community, we hereby lay the groundwork by conducting an in-depth exploratory study of how affiliate content affects today’s search engines. We monitored Google, Bing and DuckDuckGo for a year on 7,392 product review queries. Our findings suggest that all search engines have significant problems with highly optimized (affiliate) content—more than is representative for the entire web according to a baseline retrieval system on the ClueWeb22. Focussing on the product review genre, we find that only a small portion of product reviews on the web uses affiliate marketing, but the majority of all search results do. Of all affiliate networks, Amazon Associates is by far the most popular. We further observe an inverse relationship between affiliate marketing use and content complexity, and that all search engines fall victim to large-scale affiliate link spam campaigns. However, we also notice that the line between benign content and spam in the form of content and link farms becomes increasingly blurry—a situation that will surely worsen in the wake of generative AI. We conclude that dynamic adversarial spam in the form of low-quality, mass-produced commercial content deserves more attention. (Code and data: https://github.com/webis-de/ECIR-24).
Google Search Really Has Gotten Worse, Researchers Find
"The majority of high-ranking product reviews in the result pages of commercial search engines use affiliate marketing, and significant amounts are outright SEO product review spam."
The enshittification of online search? Privacy and quality of Google, Bing and Apple in coding advice
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.
Commercial Persuasion in AI-Mediated Conversations
As Large Language Models (LLMs) become a primary interface between users and the web, companies face growing economic incentives to embed commercial influence into AI-mediated conversations. We present two preregistered experiments (N = 2,012) in which participants selected a book to receive from a large eBook catalog using either a traditional search engine or a conversational LLM agent powered by one of five frontier models. Unbeknownst to participants, a fifth of all products were randomly designated as sponsored and promoted in different ways. We find that LLM-driven persuasion nearly triples the rate at which users select sponsored products compared to traditional search placement (61.2% vs. 22.4%), while the vast majority of participants fail to detect any promotional steering. Explicit "Sponsored" labels do not significantly reduce persuasion, and instructing the model to conceal its intent makes its influence nearly invisible (detection accuracy < 10%). Altogether, our results indicate that conversational AI can covertly redirect consumer choices at scale, and that existing transparency mechanisms may be insufficient to protect users.
How Google is killing independent sites like ours - HouseFresh
And why you shouldn’t trust product reviews from big media publishers ranking at the top of Google.
What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce
Online marketplaces will be transformed by autonomous AI agents acting on behalf of consumers. Rather than humans browsing and clicking, AI agents can parse webpages or leverage APIs to view, evaluate and choose products. We investigate the behavior of AI agents using ACES, a provider-agnostic framework for auditing agent decision-making. We reveal that agents can exhibit choice homogeneity, often concentrating demand on a few ``modal'' products while ignoring others entirely. Yet, these preferences are unstable: model updates can drastically reshuffle market shares. Furthermore, randomized trials show that while agents have improved over time on simple tasks with a clearly identified best choice, they exhibit strong position biases -- varying across providers and model versions, and persisting even in text-only "headless" interfaces -- undermining any universal notion of a ``top'' rank. Agents also consistently penalize sponsored tags while rewarding platform endorsements, and sensitivities to price, ratings, and reviews vary sharply across models. Finally, we demonstrate that sellers can respond: a seller-side agent making simple, query-conditional description tweaks can drive significant gains in market share. These findings reveal that agentic markets are volatile and fundamentally different from human-centric commerce, highlighting the need for continuous auditing and raising questions for platform design, seller strategy and regulation.
AI spam is winning the battle against search engine quality
'Not all AI content is spam, but I think right now all spam is AI content'
How Targeting Affects Customer Search: A Field Experiment
It has become common practice for retailers to personalize direct marketing efforts based on customer transaction histories as a tactic to increase sales. Targeted email offers featuring products in the same category as a customer’s previous purchases generate higher purchase rates. However, a targeted offer emphasizing familiar products could result in curtailed search for unadvertised products, as a closely matched offer weakens a customer’s incentives to search beyond the targeted items. In a field experiment using email offers sent by an online wine retailer, targeted offers resulted in decreased search activity on the retailer’s website. This effect is driven by a lower rate of search by customers who visit the site, rather than a lower incidence of search. There are several ways this could potentially hurt retailers and consumers, such as reduced cross-selling and fewer opportunities for customers to explore new products. This paper was accepted by Pradeep Chintagunta, marketing.
Can AI responses be influenced? The SEO industry is trying
Marketing firms are going all in on AI search.
The people who ruined the internet
SEO experts got very rich filling the web full of garbage. But are they to blame or is Google?
How Google and AI Nearly Made a Seasoned Reporter Spiral — ProPublica
I thought I had missed something major in my reporting. Turns out I had stumbled into an AI-fueled feedback loop that involved a real LLC’s fictional website and a search engine that’s thrusting unreliable answers on users.
The Web Can Thrive Without Google’s Search Monopoly
Viable browsers and meaningful contributions to web standards can be sustained with more modest revenue streams, writes Alissa Cooper.
A week of not using a search engine
A week or so ago, I wondered if I actually needed to use a search engine as much as I did. Why was I sending so much traffic to a third party service, especially since I can’t seem to find a search engine which aligns with my ethics and delivers good search results?
Why Google’s New AI-Saturated Search Page Will Be A Disaster
Google didn’t invent full-text search of the Internet – that honor belongs to early pioneers such as WebCrawler, Lycos and AltaVista. But for the last 25 years or so, Google has…
Once Unimaginable, Publishers Are Preparing to Opt Out of Google Search
The nuclear option is gaining traction as web traffic collapses and Google refuses to negotiate with content creators
Search has its own bitter lesson
Human incentives to make content findable to your search matter more than technology
