Google replied to a modest publisher whose article provided a detailed guide on how major corporate publishers manipulate the Google Reviews System Algorithm and evade consequences. This demonstrates a perceived favoritism towards large brands, resulting in adverse effects on smaller independent publishers.
HouseFresh Google Algorithm Exposé
The narrative commences with an article titled “How Google is undermining independent platforms like ours,” featured on the HouseFresh website. The post presented what it claimed to be proof that numerous corporate review platforms manipulated Google’s algorithm by creating the illusion of hands-on reviews for products that, according to HouseFresh, were not genuinely reviewed.
For instance, it highlighted how several publishers ranked a pricey air purifier, which HouseFresh (alongside Consumer Reports) had evaluated and determined to perform worse than more affordable alternatives. This purifier also consumed more energy and necessitated an annual expenditure of $199.98 for replacements. Nevertheless, prominent brand websites awarded the product glowing reviews, presumably due to the lucrative affiliate earnings associated with its high cost.
Significantly, they illustrated how product photos from various major brand publishers were sourced from the same photographer, seemingly captured in identical locations. This strongly suggested that the individual publishers did not individually review the product as implied.
HouseFresh meticulously dissected what they assert are examples of Google favoring fake reviews. Here’s a partial compilation of sites accused by HouseFresh of effectively ranking low-quality reviews:
- Better Homes & Gardens
- Real Simple
- Dotdash Meredith
- BuzzFeed
- Reddit, featuring a spam link posted by a user with a suspended account
- Popular Science
HouseFresh presented a clear and logical narrative, illustrating how Google’s Review Systems algorithms purportedly exempt major brands while small independent websites, which publish genuine reviews, experience a gradual decline in traffic with each iteration of Google’s new algorithms.
Google Responds
Google’s SearchLiaison issued a response on X (formerly Twitter), addressing the accusations with seriousness. Key points from the response include:
- Google does not conduct manual checks on claims made on webpages, except as part of a reconsideration request following a manual action.
- Google’s algorithms do not utilize phrases intended to suggest a hands-on review as a ranking signal.
Does Google Show Preference To Big Brands?
Having been immersed in SEO for 25 years, I recall a period in the early 2000s when Google exhibited a bias towards large corporate brands based on their PageRank scores. Sites with higher PageRank tended to rank for an extensive array of keywords. However, Google subsequently mitigated the influence of PageRank scores, resulting in fewer less-relevant big brand sites appearing in the search results pages (SERPs).
This wasn’t a deliberate act of bias by Google towards big brands; rather, it was a consequence of their algorithms not functioning as intended.
It’s plausible that there are currently signals within Google’s algorithm that inadvertently favor big brands. If I were to speculate on the nature of these signals, I would suggest they might relate to user interactions indicating user preferences. The recent revelations about the Navboost algorithm during the Google antitrust lawsuit underscore the significance of user interactions as a ranking-related signal.
In this scenario, if users demonstrate trust in a particular brand through their interactions (such as searching using brand names), Google may interpret these signals as indicators of user preference and subsequently rank those sites higher because they align with user expectations.
This is merely my speculation on what might be occurring—namely, that Google’s reliance on user signals may unintentionally favor big brands, a phenomenon I’ve been highlighting for years (refer to Google’s Froot Loops Algorithm).
Original news from SearchEngineJournal